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 "on multi agent learning in team sports games"):
  Abstract: In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem.

Includes members:
  Member "winning isn t everything training human like agents for playtesting and game ai":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hierarchical cooperative multi agent reinforcement learning with skill discovery")
  Member "skynet a top deep rl agent in the inaugural pommerman team competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning")
  Member "safer deep rl with shallow mcts a case study in pommerman":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning")
  Member "learning latent plans from play":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "a survey and critique of multiagent deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "multi agent adversarial inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "qmix monotonic value function factorisation for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "learning dexterous in hand manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "never give up learning directed exploration 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.MA(Multiagent Systems)