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: "distributed methods for computing approximate equilibria"
  Abstract: We present a new, distributed method to compute approximate Nash equilibria in bimatrix games. In contrast to previous approaches that analyze the two payoff matrices at the same time (for example, by solving a single LP that combines the two players’ payoffs), our algorithm first solves two independent LPs, each of which is derived from one of the two payoff matrices, and then computes an approximate Nash equilibrium using only limited communication between the players. Our method gives improved bounds on the complexity of computing approximate Nash equilibria in a number of different settings. Firstly, it gives a polynomial-time algorithm for computing approximate well supported Nash equilibria (WSNE) that always finds a 0.6528-WSNE, beating the previous best guarantee of 0.6608. Secondly, since our algorithm solves the two LPs separately, it can be applied to give an improved bound in the limited communication setting, giving a randomized expected-polynomial-time algorithm that uses poly-logarithmic communication and finds a 0.6528-WSNE, which beats the previous best known guarantee of 0.732. It can also be applied to the case of approximate Nash equilibria, where we obtain a randomized expected-polynomial-time algorithm that uses poly-logarithmic communication and always finds a 0.382-approximate Nash equilibrium, which improves the previous best guarantee of 0.438. Finally, the method can also be applied in the query complexity setting to give an algorithm that makes \(O(n \log n)\) payoff queries and always finds a 0.6528-WSNE, which improves the previous best known guarantee of 2/3.

Connected through hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria"):
  with members:
    - "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"
    - "the query complexity of correlated equilibria"
    - "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 "computing constrained approximate equilibria in polymatrix games"):
  with members:
    - "fptas for mixed strategy nash equilibria in tree graphical games and their generalizations"
    - "approximating nash equilibria in tree polymatrix games"
    - "an empirical study on computing equilibria in polymatrix games"
    - "inapproximability results for approximate nash equilibria"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "inapproximability of nash equilibrium"
    - "computing approximate nash equilibria in polymatrix games"
    - "approximate well supported nash equilibria below two thirds"

Connected through hyperedge HE-3 (induced by source paper "communication complexity of approximate nash equilibria"):
  with members:
    - "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"
    - "query complexity of approximate nash equilibria"

Connected through hyperedge HE-4 (induced by source paper "inapproximability results for approximate nash equilibria"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "am with multiple merlins"
    - "approximate well supported nash equilibria below two thirds"
    - "inapproximability of np complete variants of nash equilibrium"

Connected through hyperedge HE-5 (induced by source paper "on equilibrium computation in biased games with quadratic penalties"):
  with members:
    - "the complexity of equilibria for risk modeling valuations"
    - "on the complexity of nash equilibria in anonymous games"
    - "computing approximate nash equilibria in polymatrix games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "finding approximate nash equilibria of bimatrix games via payoff queries"):
  with members:
    - "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"
    - "query complexity of correlated equilibrium"
    - "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)