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: "efficient evolutionary dynamics with extensive form games"
  Abstract: Evolutionary game theory combines game theory and dynamical systems and is customarily adopted to describe evolutionary dynamics in multi-agent systems. In particular, it has been proven to be a successful tool to describe multi-agent learning dynamics. To the best of our knowledge, we provide in this paper the first replicator dynamics applicable to the sequence form of an extensive-form game, allowing an exponential reduction of time and space w.r.t. the currently adopted replicator dynamics for normal form. Furthermore, our replicator dynamics is realization equivalent to the standard replicator dynamics for normal form. We prove our results for both discrete-time and continuous-time cases. Finally, we extend standard tools to study the stability of a strategy profile to our replicator dynamics.

Connected through hyperedge HE-1 (induced by source paper "neural replicator dynamics"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "alpha rank multi agent evaluation by evolution"
    - "alternative function approximation parameterizations for solving games an analysis of f regression counterfactual regret minimization"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "revisiting cfr and alternating updates"
    - "deep counterfactual regret minimization"

Connected through hyperedge HE-2 (induced by source paper "actor critic policy optimization in partially observable multiagent environments"):
  with members:
    - "combining no regret and q learning"
    - "introduction to online convex optimization"
    - "policy gradient with value function approximation for collective multiagent planning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "a generalised method for empirical game theoretic analysis"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "maintaining cooperation in complex social dilemmas using deep 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.GT(Computer Science and Game Theory)