human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "understanding the impact of entropy on policy optimization"
  Abstract: Entropy regularization is commonly used to improve policy optimization in reinforcement learning. It is believed to help with \emph{exploration} by encouraging the selection of more stochastic policies. In this work, we analyze this claim using new visualizations of the optimization landscape based on randomly perturbing the loss function. We first show that even with access to the exact gradient, policy optimization is difficult due to the geometry of the objective function. Then, we qualitatively show that in some environments, a policy with higher entropy can make the optimization landscape smoother, thereby connecting local optima and enabling the use of larger learning rates. This paper presents new tools for understanding the optimization landscape, shows that policy entropy serves as a regularizer, and highlights the challenge of designing general-purpose policy optimization algorithms.

Connected through hyperedge HE-1 (induced by source paper "discor corrective feedback in reinforcement learning via distribution correction"):
  with members:
    - "gradient surgery for multi task learning"
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "towards characterizing divergence in deep q learning"
    - "behavior regularized offline reinforcement learning"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "stabilizing off policy q learning via bootstrapping error reduction"
    - "provably efficient q learning with function approximation via distribution shift error checking oracle"
    - "ray interference a source of plateaus in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling"):
  with members:
    - "diagnosing bottlenecks in deep q learning algorithms"
    - "benchmarking model based reinforcement learning"
    - "remember and forget for experience replay"
    - "supervised policy update for deep reinforcement learning"
    - "clipped action policy gradient"
    - "marginal policy gradients a unified family of estimators for bounded action spaces with applications"
    - "curiosity driven experience prioritization via density estimation"
    - "safe exploration in continuous action spaces"

Connected through hyperedge HE-3 (induced by source paper "observational overfitting in reinforcement learning"):
  with members:
    - "generalization in deep networks the role of distance from initialization"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "weight agnostic neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"

Connected through hyperedge HE-4 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning"):
  with members:
    - "regularized anderson acceleration for off policy deep reinforcement learning"
    - "policy consolidation for continual reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "a theory of regularized markov decision processes"
    - "importance resampling for off policy prediction"
    - "lookahead optimizer k steps forward 1 step back"
    - "understanding short horizon bias in stochastic meta optimization"
    - "the mirage of action dependent baselines in reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "dynamic scale inference by entropy minimization"):
  with members:
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "semi supervised domain adaptation via minimax entropy"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "on calibration of modern neural networks"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "deep layer aggregation"
    - "deformable convolutional networks"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-6 (induced by source paper "if maxent rl is the answer what is the question"):
  with members:
    - "end to end robotic reinforcement learning without reward engineering"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient exploration via state marginal matching"
    - "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "composable deep reinforcement learning for robotic manipulation"
    - "unsupervised control through non parametric discriminative rewards"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-7 (induced by source paper "optimality and approximation with policy gradient methods in markov decision processes"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "information theoretic considerations in batch reinforcement learning"
    - "a theory of regularized markov decision processes"
    - "global optimality guarantees for policy gradient methods"
    - "global convergence of policy gradient methods for the linear quadratic regulator"
    - "on oracle efficient pac rl with rich observations"
    - "global convergence of policy gradient methods for linearized control problems"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-8 (induced by source paper "marginalized state distribution entropy regularization in policy optimization"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "provably efficient maximum entropy exploration"
    - "count based exploration with the successor representation"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.LG(Machine Learning)