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: "multi agent reinforcement learning a report on challenges and approaches"
  Abstract: Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term. This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like \textit{Go}. While RL is emerging as a practical component in real-life systems, most successes have been in Single Agent domains. This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments. The report concludes with advances in the paradigm of training Multi-Agent Systems called \textit{Decentralized Actor, Centralized Critic}, based on an extension of MDPs called \textit{Decentralized Partially Observable MDP}s, which has seen a renewed interest lately.

Connected through hyperedge HE-1 (induced by source paper "a general framework for learning mean field games"):
  with members:
    - "q learning for mean field controls"
    - "adaptive trust region policy optimization global convergence and faster rates for regularized mdps"
    - "natural actor critic converges globally for hierarchical linear quadratic regulator"
    - "a theory of regularized markov decision processes"
    - "real time bidding by reinforcement learning in display advertising"
    - "reinforcement learning with deep energy based policies"
    - "a unified view of entropy regularized markov decision processes"
    - "markov nash equilibria in mean field games with discounted cost"

Connected through hyperedge HE-2 (induced by source paper "real time tree search with pessimistic scenarios"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "continual match based training in pommerman technical report"
    - "backplay man muss immer umkehren"
    - "pommerman a multi agent playground"
    - "how to combine tree search methods in reinforcement learning"
    - "feedback based tree search for reinforcement learning"
    - "thinking fast and slow with deep learning and tree search"
    - "the ff planning system fast plan generation through heuristic search"

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.AI(Artificial Intelligence)