human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "measuring the intrinsic dimension of objective landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving generalization by controlling label noise information in neural network weights")
  Member "the mirage of action dependent baselines in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning")
  Member "clipped action policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "striving for simplicity and performance in off policy drl output normalization and non uniform sampling")
  Member "smoothed action value functions for learning gaussian policies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning")
  Member "the goldilocks zone towards better understanding of neural network loss landscapes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the break even point on optimization trajectories of deep neural networks")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fast efficient hyperparameter tuning for policy gradients")
  Member "are deep policy gradient algorithms truly policy gradient algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ready policy one world building through active learning")

Question: Please tell me which class the source paper 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)