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 "modeling others using oneself in multi agent reinforcement learning"):
  Abstract: We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.

Includes members:
  Member "pragmatic pedagogic value alignment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")
  Member "maintaining cooperation in complex social dilemmas using deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "other play for zero shot coordination")
  Member "learning with opponent learning awareness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "emergence of grounded compositional language in multi agent populations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication")
  Member "multi agent reinforcement learning in sequential social dilemmas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "deep decentralized multi task multi agent reinforcement learning under partial observability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchically decoupled imitation for morphological transfer")
  Member "inverse reward design":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "safe imitation learning via fast bayesian reward inference from preferences")

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)