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: "individual planning in agent populations exploiting anonymity and frame action hypergraphs"
  Abstract: Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.

Connected through hyperedge HE-1 (induced by source paper "policy gradient with value function approximation for collective multiagent planning"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "learning functions across many orders of magnitudes"
    - "asynchronous methods for deep reinforcement learning"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "trust region policy optimization"
    - "learning to cooperate via policy search"
    - "planning for decentralized control of multiple robots under uncertainty"

Connected through hyperedge HE-2 (induced by source paper "a survey of learning in multiagent environments dealing with non stationarity"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "belief and truth in hypothesised behaviours"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"
    - "opponent modeling in deep reinforcement learning"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"

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)