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 "revisiting the softmax bellman operator new benefits and new perspective"):
  Abstract: The impact of softmax on the value function itself in reinforcement learning (RL) is often viewed as problematic because it leads to sub-optimal value (or Q) functions and interferes with the contraction properties of the Bellman operator. Surprisingly, despite these concerns, and independent of its effect on exploration, the softmax Bellman operator when combined with Deep Q-learning, leads to Q-functions with superior policies in practice, even outperforming its double Q-learning counterpart. To better understand how and why this occurs, we revisit theoretical properties of the softmax Bellman operator, and prove that $(i)$ it converges to the standard Bellman operator exponentially fast in the inverse temperature parameter, and $(ii)$ the distance of its Q function from the optimal one can be bounded. These alone do not explain its superior performance, so we also show that the softmax operator can reduce the overestimation error, which may give some insight into why a sub-optimal operator leads to better performance in the presence of value function approximation. A comparison among different Bellman operators is then presented, showing the trade-offs when selecting them.

Includes members:
  Member "rainbow combining improvements in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "equivalence between policy gradients and soft q learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a distributional perspective on reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "distributional reinforcement learning with quantile regression":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial intelligence for prosthetics challenge solutions")
  Member "bridging the gap between value and policy based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  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 "reinforcement learning with deep energy based policies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "a unified view of entropy regularized markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reinforcement learning via fenchel rockafellar duality")

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)