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: "multiagent learning in large anonymous games"
  Abstract: In large systems, it is important for agents to learn to act effectively, but sophisticated multi-agent learning algorithms generally do not scale. An alternative approach is to find restricted classes of games where simple, efficient algorithms converge. It is shown that stage learning efficiently converges to Nash equilibria in large anonymous games if best-reply dynamics converge. Two features are identified that improve convergence. First, rather than making learning more difficult, more agents are actually beneficial in many settings. Second, providing agents with statistical information about the behavior of others can significantly reduce the number of observations needed.

Connected through hyperedge HE-1 (induced by source paper "decentralised learning in systems with many many strategic agents"):
  with members:
    - "multi agent reinforcement learning in sequential social dilemmas"
    - "spectrum sharing as spatial congestion games"

Connected through hyperedge HE-2 (induced by source paper "query complexity of approximate equilibria in anonymous games"):
  with members:
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "optimal learning via the fourier transform for sums of independent integer random variables"

Connected through hyperedge HE-3 (induced by source paper "comparison of multi agent and single agent inverse learning on a simulated soccer example"):
  with members:
    - "multi agent inverse reinforcement learning for zero sum games"
    - "computational rationalization the inverse equilibrium problem"
    - "inverse reinforcement learning with gaussian process"
    - "a multiagent reinforcement learning algorithm with non linear dynamics"

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)