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: "the query complexity of correlated equilibria"
  Abstract: We consider the complexity of finding a Correlated Equilibrium in an n-player game in a model that allows the algorithm to make queries for players' utilities at pure strategy profiles. Many randomized regret-matching dynamics are known to yield an approximate correlated equilibrium quickly: in time that is polynomial in the number of players, n, the number of strategies of each player, m, and the approximation error, 1/?. Here we show that both randomization and approximation are necessary: no efficient deterministic algorithm can reach even an approximate equilibrium and no efficient randomized algorithm can reach an exact equilibrium.

Connected through hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the communication complexity of local search"
    - "complexity theory game theory and economics"
    - "communication complexity of correlated equilibrium in two player games"
    - "query to communication lifting for bpp"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"

Connected through hyperedge HE-2 (induced by source paper "learning convex partitions and computing game theoretic equilibria from best response queries"):
  with members:
    - "communication complexity of approximate nash equilibria"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "query complexity of approximate equilibria in anonymous games"
    - "learning equilibria of games via payoff queries"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "on the approximation performance of fictitious play in finite games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-3 (induced by source paper "settling the complexity of computing approximate two player nash equilibria"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "detecting communities is hard and counting them is even harder"
    - "approximating nash equilibria in tree polymatrix games"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability results for approximate nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "near optimal hardness results for signaling in bayesian games"
    - "eth hardness for densest k subgraph with perfect completeness"

Connected through hyperedge HE-4 (induced by source paper "communication complexity of approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "inapproximability of nash equilibrium"
    - "computational complexity of approximate nash equilibrium in large games"
    - "learning equilibria of games via payoff queries"
    - "on the communication complexity of approximate nash equilibria"
    - "query complexity of approximate nash equilibria"

Connected through hyperedge HE-5 (induced by source paper "well supported versus approximate nash equilibria query complexity of large games"):
  with members:
    - "on the structure covering and learning of poisson multinomial distributions"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "on the complexity of nash equilibria in anonymous games"
    - "inapproximability of nash equilibrium"
    - "query complexity of approximate equilibria in anonymous games"
    - "constant rank bimatrix games are ppad hard"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"

Connected through hyperedge HE-6 (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"
    - "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"
    - "on the complexity of nash equilibria in anonymous games"

Connected through hyperedge HE-7 (induced by source paper "inapproximability of nash equilibrium"):
  with members:
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "computational complexity of approximate nash equilibrium in large games"
    - "computing approximate nash equilibria in polymatrix games"
    - "the complexity of fairness through equilibrium"
    - "query complexity of approximate nash equilibria"
    - "the complexity of non monotone markets"
    - "settling the complexity of arrow debreu equilibria in markets with additively separable utilities"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "achieving target equilibria in network routing games without knowing the latency functions"):
  with members:
    - "computing optimal tolls in routing games without knowing the latency functions"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "learning equilibria of games via payoff queries"
    - "the network improvement problem for equilibrium routing"
    - "efficiently learning from revealed preference"
    - "polynomial time computation of exact correlated equilibrium in compact 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)