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: "cesma centralized expert supervises multi agents"
  Abstract: We consider the reinforcement learning problem of training multiple agents in order to maximize a shared reward. In this multi-agent system, each agent seeks to maximize the reward while interacting with other agents, and they may or may not be able to communicate. Typically the agents do not have access to other agent policies and thus each agent observes a non-stationary and partially-observable environment. In order to obtain multi-agents that act in a decentralized manner, we introduce a novel algorithm under the framework of centralized learning, but decentralized execution. This training framework first obtains solutions to a multi-agent problem with a single centralized joint-space learner. This centralized expert is then used to guide imitation learning for independent decentralized multi-agents. This framework has the flexibility to use any reinforcement learning algorithm to obtain the expert as well as any imitation learning algorithm to obtain the decentralized agents. This is in contrast to other multi-agent learning algorithms that, for example, can require more specific structures. We present some theoretical error bounds for our method, and we show that one can obtain decentralized solutions to a multi-agent problem through imitation learning.

Connected through hyperedge HE-1 (induced by source paper "maven multi agent variational exploration"):
  with members:
    - "the starcraft multi agent challenge"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "virel a variational inference framework for reinforcement learning"
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "coordinated exploration in concurrent reinforcement learning"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-2 (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"

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)