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 "entropic regularization of markov decision processes"):
  Abstract: An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment. Such interactive data gathering commonly leads to divergence towards dangerous or uninformative regions of the state space unless additional regularization measures are taken. Prior works proposed bounding the information loss measured by the Kullback–Leibler (KL) divergence at every policy improvement step to eliminate instability in the learning dynamics. In this paper, we consider a broader family of f-divergences, and more concretely    α   -divergences, which inherit the beneficial property of providing the policy improvement step in closed form at the same time yielding a corresponding dual objective for policy evaluation. Such entropic proximal policy optimization view gives a unified perspective on compatible actor-critic architectures. In particular, common least-squares value function estimation coupled with advantage-weighted maximum likelihood policy improvement is shown to correspond to the Pearson     χ 2    -divergence penalty. Other actor-critic pairs arise for various choices of the penalty-generating function f. On a concrete instantiation of our framework with the    α   -divergence, we carry out asymptotic analysis of the solutions for different values of    α    and demonstrate the effects of the divergence function choice on common standard reinforcement learning problems.

Includes members:
  Member "a theory of regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "tsallis reinforcement learning a unified framework for maximum entropy reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "if maxent rl is the answer what is the question")
  Member "maximum causal tsallis entropy imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "correlated adversarial imitation learning")
  Member "an elementary introduction to information geometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the alpha divergences associated with a pair of strictly comparable quasi arithmetic means")
  Member "path consistency learning in tsallis entropy regularized mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi path policy optimization")
  Member "sparse markov decision processes with causal sparse tsallis entropy regularization for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "off policy actor critic in an ensemble achieving maximum general entropy and effective environment exploration in deep reinforcement learning")
  Member "a unified view of entropy regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning via fenchel rockafellar duality")
  Member "proximal policy optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep reinforcement learning for autonomous driving a survey")

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)