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: "on the value of correlation"
  Abstract: Correlated equilibrium (Aumann, 1974) generalizes Nash equilibrium to allow correlation devices. Aumann showed an example of a game, and of a correlated equilibrium in this game, in which the agents' surplus (expected sum of payo s) is greater than their surplus in all mixed-strategy equilibria. Following the idea initiated by the price of anarchy literature (Koutsoupias & Papadimitriou, 1999;Papadimitriou, 2001) this suggests the study of two major measures for the value of correlation in a game with non-negative payoffs: 1. The ratio between the maximal surplus obtained in a correlated equilibrium to the maximal surplus obtained in a mixed-strategy equilibrium. We refer to this ratio as the mediation value. 2. The ratio between the maximal surplus to the maximal surplus obtained in a correlated equilibrium. We refer to this ratio as the enforcement value. In this work we initiate the study of the mediation and enforcement values, providing several general results on the value of correlation as captured by these concepts. We also present a set of results for the more specialized case of congestion games (Rosenthal,1973), a class of games that received a lot of attention in the recent literature.

Connected through hyperedge HE-1 (induced by source paper "signal instructed coordination in cooperative multi agent reinforcement learning"):
  with members:
    - "on the pitfalls of measuring emergent communication"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "efficient regret minimization algorithm for extensive form correlated equilibrium"):
  with members:
    - "introduction to online convex optimization"
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "revisiting cfr and alternating updates"
    - "solving large extensive form games with strategy constraints"
    - "safe and nested subgame solving for imperfect information games"
    - "regret minimization in behaviorally constrained zero sum games"

Connected through hyperedge HE-3 (induced by source paper "correlation in extensive form games saddle point formulation and benchmarks"):
  with members:
    - "introduction to online convex optimization"
    - "solving imperfect information games via discounted regret minimization"
    - "online convex optimization for sequential decision processes and extensive form games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "a sampling based approach to computing equilibria in succinct extensive form games"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-4 (induced by source paper "coarse correlation in extensive form games"):
  with members:
    - "correlation in extensive form games saddle point formulation and benchmarks"
    - "a sampling based approach to computing equilibria in succinct extensive form games"

Connected through hyperedge HE-5 (induced by source paper "team maxmin equilibrium efficiency bounds and algorithms"):
  with members:
    - "approximability and parameterized complexity of minmax values"
    - "simple strategies for large zero sum games with applications to complexity theory"

Connected through hyperedge HE-6 (induced by source paper "computing equilibria with partial commitment"):
  with members:
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-7 (induced by source paper "the power of mediation in an extended el farol game"):
  with members:
    - "lower bounds on implementing robust and resilient mediators"

Connected through hyperedge HE-8 (induced by source paper "selfishness level of strategic games"):
  with members:
    - "approximate strong equilibrium in job scheduling games"
    - "approaching utopia strong truthfulness and externality resistant mechanisms"
    - "the robust price of anarchy of altruistic games"
    - "mechanisms for making crowds truthful"
    - "altruism in atomic congestion games"

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)