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: "query complexity of correlated equilibrium"
  Abstract: We study lower bounds on the query complexity of determining correlated equilibrium. In particular, we consider a query model in which an n-player game is specified via a black box that returns players' utilities at pure action profiles. In this model we establish that in order to compute a correlated equilibrium any deterministic algorithm must query the black box an exponential (in n) number of times.

Connected through hyperedge HE-1 (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"
    - "the query complexity of correlated equilibria"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "communication complexity of approximate nash equilibria"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the query complexity of correlated 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"

Connected through hyperedge HE-3 (induced by source paper "the query complexity of correlated equilibria"):
  with members:
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "learning equilibria of games via payoff queries"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "simple strategies for large zero sum games with applications to complexity theory"

Connected through hyperedge HE-4 (induced by source paper "logarithmic query complexity for approximate nash computation in large games"):
  with members:
    - "query complexity of approximate equilibria in anonymous games"
    - "query complexity of approximate nash equilibria"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-5 (induced by source paper "well supported versus approximate nash equilibria query complexity of large games"):
  with members:
    - "the query complexity of correlated equilibria"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "learning equilibria of games via payoff queries"):
  with members:
    - "the query complexity of correlated equilibria"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "graphical models for game theory"
    - "on the communication complexity of approximate nash equilibria"
    - "query complexity of approximate nash equilibria"
    - "approximate well supported nash equilibria below two thirds"
    - "on the complexity of trial and error"
    - "on oblivious ptas s for nash equilibrium"

Connected through hyperedge HE-7 (induced by source paper "query complexity of approximate nash equilibria"):
  with members:
    - "the query complexity of correlated equilibria"
    - "inapproximability of nash equilibrium"
    - "computational complexity of approximate nash equilibrium in large games"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"
    - "approximate nash equilibria via sampling"
    - "weak calibration is computationally hard"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-8 (induced by source paper "finding approximate nash equilibria of bimatrix games via payoff queries"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the query complexity of correlated equilibria"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "query complexity of approximate equilibria in anonymous games"
    - "learning equilibria of games via payoff queries"
    - "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3"
    - "query complexity of approximate nash equilibria"
    - "approximate well supported nash equilibria below two thirds"

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)