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: "the representational capacity of action value networks for multi agent reinforcement learning"
  Abstract: Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. However, given the lack of theoretical insight, it remains unclear what the employed neural networks are learning, or how we should enhance their representational power to address the problems on which they fail. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial problems that arise in the multi-agent setting, such as an exponential number of joint actions or the lack of an explicit coordination mechanism. Our results quantify how well various approaches can represent the requisite value functions, and help us identify issues that can impede good performance.

Connected through hyperedge HE-1 (induced by source paper "deep coordination graphs"):
  with members:
    - "the starcraft multi agent challenge"
    - "a review of cooperative multi agent deep reinforcement learning"
    - "exploration with unreliable intrinsic reward in multi agent reinforcement learning"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "multiagent soft q learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-2 (induced by source paper "hierarchical critics assignment for multi agent reinforcement learning"):
  with members:
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "data efficient hierarchical reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "unity a general platform for intelligent agents"
    - "meta learning shared hierarchies"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-3 (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"
    - "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"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"

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)