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 "regularised deep reinforcement learning with guaranteed convergence"):
  Abstract: Deep Q-networks (DQNs) suffer from two important challenges hindering their application in real-world scenarios. First, DQNs overestimate Q-values which leads to increased sample complexity, and second, no theoretical convergence guarantees have been established. In this paper, we address both problems by introducing an intrinsic penalty signal arising from a Kullback-Leibler (KL) constraint that encourages reduced Q-value estimates. We then prove, for the first time, convergence to a stationary point under a specific scheduling of the penalisation magnitude. Our proofs operate in the deep reinforcement learning setting that considers convolutional and dense layers for Q-function approximation. Furthermore, we prove divergence of standard DQNs using a counter example that relates to the non-optimal choice of the history-scheduling parameter adopted by `vanilla' DQNs. We believe this can shed the light on some of the difficulties reported by researchers and practitioners in the field.

Includes members:
  Member "decentralised learning in systems with many many strategic agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi type mean field reinforcement learning")
  Member "an information theoretic on line update principle for perception action coupling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an information theoretic on line learning principle for specialization in hierarchical decision making systems")
  Member "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to walk in the real world with minimal human effort")
  Member "balancing two player stochastic games with soft q learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "rainbow combining improvements in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "bridging the gap between value and policy based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "reinforcement learning with deep energy based policies":
    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.AI(Artificial Intelligence)