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: "decentralised learning in systems with many many strategic agents"
  Abstract: Although multi-agent reinforcement learning can tackle systems of strategically interacting entities, it currently fails in scalability and lacks rigorous convergence guarantees. Crucially, learning in multi-agent systems can become intractable due to the explosion in the size of the state-action space as the number of agents increases. In this paper, we propose a method for computing closed-loop optimal policies in multi-agent systems that scales independently of the number of agents. This allows us to show, for the first time, successful convergence to optimal behaviour in systems with an unbounded number of interacting adaptive learners. Studying the asymptotic regime of N-player stochastic games, we devise a learning protocol that is guaranteed to converge to equilibrium policies even when the number of agents is extremely large. Our method is model-free and completely decentralised so that each agent need only observe its local state information and its realised rewards. We validate these theoretical results by showing convergence to Nash-equilibrium policies in applications from economics and control theory with thousands of strategically interacting agents.

Connected through hyperedge HE-1 (induced by source paper "multi type mean field reinforcement learning"):
  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"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-2 (induced by source paper "coordinating the crowd inducing desirable equilibria in non cooperative systems"):
  with members:
    - "learning parametric closed loop policies for markov potential games"
    - "balancing two player stochastic games with soft q learning"
    - "prosocial learning agents solve generalized stag hunts better than selfish ones"
    - "dynamic potential games in communications fundamentals and applications"
    - "spectrum sharing as spatial congestion games"

Connected through hyperedge HE-3 (induced by source paper "regularised deep reinforcement learning with guaranteed convergence"):
  with members:
    - "an information theoretic on line update principle for perception action coupling"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "balancing two player stochastic games with soft q learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "reinforcement learning with deep energy based policies"
    - "proximal policy optimization algorithms"

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)