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 "agent modeling as auxiliary task for deep reinforcement learning"):
  Abstract: In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling. Inspired by recent works on representation learning and multiagent deep reinforcement learning, we propose two architectures to perform agent modeling: the first one based on parameter sharing, and the second one based on agent policy features. Both architectures aim to learn other agents' policies as auxiliary tasks, besides the standard actor (policy) and critic (values). We performed experiments in both cooperative and competitive domains. The former is a problem of coordinated multiagent object transportation and the latter is a two-player mini version of the Pommerman game. Our results show that the proposed architectures stabilize learning and outperform the standard A3C architecture when learning a best response in terms of expected rewards.

Includes members:
  Member "a geometric perspective on optimal representations for reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepmdp learning continuous latent space models for representation learning")
  Member "a survey and critique of multiagent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "backplay man muss immer umkehren":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "go explore a new approach for hard exploration problems")
  Member "deep reinforcement learning for general video game ai":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fully differentiable procedural content generation through generative playing networks")
  Member "modeling others using oneself in multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster based social reinforcement learning")
  Member "autonomous agents modelling other agents a comprehensive survey and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "machine theory of mind":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "a deep policy inference q network for multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed policy iteration for scalable approximation of cooperative multi agent policies")

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.MA(Multiagent Systems)