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 "multi agent generalized recursive reasoning"):
  Abstract: We propose a new reasoning protocol called generalized recursive reasoning (GR2), and embed it into the multi-agent reinforcement learning (MARL) framework. The GR2 model defines reasoning categories: level-$0$ agent acts randomly, and level-$k$ agent takes the best response to a mixed type of agents that are distributed over level $0$ to $k-1$. The GR2 leaners can take into account the bounded rationality, and it does not need the assumption that the opponent agents play Nash strategy in all stage games, which many MARL algorithms require. We prove that when the level $k$ is large, the GR2 learners will converge to at least one Nash Equilibrium (NE). In addition, if lower-level agents play the NE, high-level agents will surely follow as well. We evaluate the GR2 Soft Actor-Critic algorithms in a series of games and high-dimensional environment; results show that the GR2 methods have faster convergence speed than strong MARL baselines.

Includes members:
  Member "probabilistic recursive reasoning for multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "reinforcement learning and control as probabilistic inference tutorial and review":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  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 "machine theory of mind":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "multiagent soft q learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent interactions modeling with correlated policies")
  Member "reinforcement learning with deep energy based policies":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "multi agent actor critic for mixed cooperative competitive environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "density estimation using real nvp":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)