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: "deep multi agent reinforcement learning with relevance graphs"
  Abstract: Over recent years, deep reinforcement learning has shown strong successes in complex single-agent tasks, and more recently this approach has also been applied to multi-agent domains. In this paper, we propose a novel approach, called MAGnet, to multi-agent reinforcement learning (MARL) that utilizes a relevance graph representation of the environment obtained by a self-attention mechanism, and a message-generation technique inspired by the NerveNet architecture. We applied our MAGnet approach to the Pommerman game and the results show that it significantly outperforms state-of-the-art MARL solutions, including DQN, MADDPG, and MCTS.

Connected through hyperedge HE-1 (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"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-2 (induced by source paper "improving sat solver heuristics with graph networks and reinforcement learning"):
  with members:
    - "reinforcement learning driven heuristic optimization"
    - "guiding high performance sat solvers with unsat core predictions"
    - "learning transferable cooperative behavior in multi agent teams"
    - "graph representations for higher order logic and theorem proving"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "learning a sat solver from single bit supervision"
    - "graph networks as learnable physics engines for inference and control"

Connected through hyperedge HE-3 (induced by source paper "learning structured communication for multi agent reinforcement learning"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "multi agent actor critic with hierarchical graph attention network"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "graph convolutional reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-5 (induced by source paper "qmix monotonic value function factorisation for deep multi agent reinforcement learning"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "smix lambda enhancing centralized value functions for cooperative multi agent reinforcement learning"
    - "deep multi agent reinforcement learning with discrete continuous hybrid action spaces"
    - "the representational capacity of action value networks for multi agent reinforcement learning"
    - "hierarchical cooperative multi agent reinforcement learning with skill discovery"
    - "learning nearly decomposable value functions via communication minimization"
    - "the starcraft multi agent challenge"
    - "deep coordination graphs"

Connected through hyperedge HE-6 (induced by source paper "graph convolutional reinforcement learning"):
  with members:
    - "learning transferable cooperative behavior in multi agent teams"
    - "value propagation for decentralized networked deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "relational deep reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "magent a many agent reinforcement learning platform for artificial collective intelligence"

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)