chore: [pre-commit.ci] pre-commit autoupdate (#175)

* chore: [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/asottile/pyupgrade: v3.8.0 → v3.10.1](asottile/pyupgrade@v3.8.0...v3.10.1)

* update recent.md to contain August

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Claudius Krause <[email protected]>

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
     - id: trailing-whitespace
 
 -   repo: https://github.com/asottile/pyupgrade
-    rev: v3.8.0
+    rev: v3.10.1
     hooks:
     - id: pyupgrade
       args: ["--py37-plus"]

docs/recent.md

@@ -62,27 +62,3 @@ This is an automatically compiled list of papers which have been added to the li
 * [$\Sigma$ Resonances from a Neural Network-based Partial Wave Analysis on $K^-p$ Scattering](https://arxiv.org/abs/2305.01852)
 * [MLAnalysis: An open-source program for high energy physics analyses](https://arxiv.org/abs/2305.00964)
 
-## April 2023
-* [Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning](https://arxiv.org/abs/2305.00493)
-* [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963)
-* [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
-* [Machine learning method for $^{12}$C event classification and reconstruction in the active target time-projection chamber](https://arxiv.org/abs/2304.13233)
-* [A Modern Global Extraction of the Sivers Function](https://arxiv.org/abs/2304.14328)
-* [Label-free timing analysis of modularized nuclear detectors with physics-constrained deep learning](https://arxiv.org/abs/2304.11930)
-* [Gauge-equivariant pooling layers for preconditioners in lattice QCD](https://arxiv.org/abs/2304.10438)
-* [Uncovering doubly charged scalars with dominant three-body decays using machine learning](https://arxiv.org/abs/2304.09195)
-* [Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models](https://arxiv.org/abs/2304.09208)
-* [Graph neural networks at the Large Hadron Collider](https://doi.org/10.1038/s42254-023-00569-0)
-* [Evidence of the Schwinger mechanism from lattice QCD](https://arxiv.org/abs/2304.07800)
-* [Research on the distribution formula of QCD strong coupling constant in medium and high energy scale region based on symbolic regression algorithm](https://arxiv.org/abs/2304.07682)
-* [Jet substructure observables for jet quenching in Quark Gluon Plasma: a Machine Learning driven analysis](https://arxiv.org/abs/2304.07196)
-* [A variational Monte Carlo algorithm for lattice gauge theories with continuous gauge groups: a study of (2+1)-dimensional compact QED with dynamical fermions at finite density](https://arxiv.org/abs/2304.05916)
-* [Equivariant Graph Neural Networks for Charged Particle Tracking](https://arxiv.org/abs/2304.05293)
-* [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836)
-* [Probing Dark QCD Sector through the Higgs Portal with Machine Learning at the LHC](https://arxiv.org/abs/2304.03237)
-* [Locality-constrained autoregressive cum conditional normalizing flow for lattice field theory simulations](https://arxiv.org/abs/2304.01798)
-* [Search for vector-like leptons at a Muon Collider](https://arxiv.org/abs/2304.01885)
-* [Searching for anomalous quartic gauge couplings at muon colliders using principle component analysis](https://arxiv.org/abs/2304.01505)
-* [Fast Point Cloud Generation with Diffusion Models in High Energy Physics](https://arxiv.org/abs/2304.01266)
-* [Invariant mass reconstruction of heavy gauge bosons decaying to $\tau$ leptons using machine learning techniques](https://arxiv.org/abs/2304.01126)
-