human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pommerman a multi agent playground"
  Abstract: We present Pommerman, a multi-agent environment based on the classic console game Bomberman. Pommerman consists of a set of scenarios, each having at least four players and containing both cooperative and competitive aspects. We believe that success in Pommerman will require a diverse set of tools and methods, including planning, opponent/teammate modeling, game theory, and communication, and consequently can serve well as a multi-agent benchmark. To date, we have already hosted one competition, and our next one will be featured in the NIPS 2018 competition track.

Connected through hyperedge HE-1 (induced by source paper "competitive experience replay"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "backplay man muss immer umkehren"
    - "learning dexterous in hand manipulation"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "large scale study of curiosity driven learning"
    - "forward backward reinforcement learning"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "barc backward reachability curriculum for robotic reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "obstacle tower a generalization challenge in vision control and planning"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "unity a general platform for intelligent agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-3 (induced by source paper "the starcraft multi agent challenge"):
  with members:
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "multi agent common knowledge reinforcement learning"
    - "value propagation networks"
    - "mean field multi agent reinforcement learning"
    - "tstarbots defeating the cheating level builtin ai in starcraft ii in the full game"
    - "on reinforcement learning for full length game of starcraft"

Connected through hyperedge HE-4 (induced by source paper "safer deep rl with shallow mcts a case study in pommerman"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "real time tree search with pessimistic scenarios"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "monte carlo tree search for simulation based strategy analysis"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"

Connected through hyperedge HE-5 (induced by source paper "evolutionary reinforcement learning for sample efficient multiagent coordination"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "emergent coordination through competition"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "counterfactual multi agent policy gradients"
    - "a multi agent reinforcement learning model of common pool resource appropriation"

Connected through hyperedge HE-6 (induced by source paper "learning transferable cooperative behavior in multi agent teams"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "attention is all you need"
    - "emergence of grounded compositional language in multi agent populations"
    - "neural message passing for quantum chemistry"
    - "vain attentional multi agent predictive modeling"

Connected through hyperedge HE-7 (induced by source paper "accelerating training in pommerman with imitation and reinforcement learning"):
  with members:
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "real time tree search with pessimistic scenarios"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "continual match based training in pommerman technical report"
    - "backplay man muss immer umkehren"
    - "deep multi agent reinforcement learning with relevance graphs"
    - "sim to real reinforcement learning for deformable object manipulation"

Connected through hyperedge HE-8 (induced by source paper "action guidance with mcts for deep reinforcement learning"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "wisemove a framework for safe deep reinforcement learning for autonomous driving"
    - "monte carlo tree search for simulation based strategy analysis"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "deep reinforcement learning a brief survey"
    - "deep reinforcement learning an overview"
    - "deep q learning from demonstrations"

Question: Please tell me which class the center node 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)