human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "finding approximate nash equilibria of bimatrix games via payoff queries"):
  Abstract: We study the deterministic and randomized query complexity of finding approximate equilibria in bimatrix games. We show that the deterministic query complexity of finding an $\epsilon$-Nash equilibrium when $\epsilon < \frac{1}{2}$ is $\Omega(k^2)$, even in zero-one constant-sum games. In combination with previous results \cite{FGGS13}, this provides a complete characterization of the deterministic query complexity of approximate Nash equilibria. We also study randomized querying algorithms. We give a randomized algorithm for finding a $(\frac{3 - \sqrt{5}}{2} + \epsilon)$-Nash equilibrium using $O(\frac{k \cdot \log k}{\epsilon^2})$ payoff queries, which shows that the $\frac{1}{2}$ barrier for deterministic algorithms can be broken by randomization. For well-supported Nash equilibria (WSNE), we first give a randomized algorithm for finding an $\epsilon$-WSNE of a zero-sum bimatrix game using $O(\frac{k \cdot \log k}{\epsilon^4})$ payoff queries, and we then use this to obtain a randomized algorithm for finding a $(\frac{2}{3} + \epsilon)$-WSNE in a general bimatrix game using $O(\frac{k \cdot \log k}{\epsilon^4})$ payoff queries. Finally, we initiate the study of lower bounds against randomized algorithms in the context of bimatrix games, by showing that randomized algorithms require $\Omega(k^2)$ payoff queries in order to find a $\frac{1}{6k}$-Nash equilibrium, even in zero-one constant-sum games. In particular, this rules out query-efficient randomized algorithms for finding exact Nash equilibria.

Includes members:
  Member "distributed methods for computing approximate equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "the query complexity of correlated equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "well supported versus approximate nash equilibria query complexity of large games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "query complexity of approximate equilibria in anonymous games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning convex partitions and computing game theoretic equilibria from best response queries")
  Member "learning equilibria of games via payoff queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiagent evaluation under incomplete information")
  Member "polylogarithmic supports are required for approximate well supported nash equilibria below 2 3":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")
  Member "query complexity of approximate nash equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "query complexity of correlated equilibrium":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "settling the complexity of computing approximate two player nash equilibria")

Question: Please tell me which class the source paper 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)