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: "computing approximate nash equilibria in polymatrix games"
  Abstract: In an $\epsilon$-Nash equilibrium, a player can gain at most $\epsilon$ by unilaterally changing his behaviour. For two-player (bimatrix) games with payoffs in $[0,1]$, the best-known$\epsilon$ achievable in polynomial time is 0.3393. In general, for $n$-player games an $\epsilon$-Nash equilibrium can be computed in polynomial time for an $\epsilon$ that is an increasing function of $n$ but does not depend on the number of strategies of the players. For three-player and four-player games the corresponding values of $\epsilon$ are 0.6022 and 0.7153, respectively. Polymatrix games are a restriction of general $n$-player games where a player's payoff is the sum of payoffs from a number of bimatrix games. There exists a very small but constant $\epsilon$ such that computing an $\epsilon$-Nash equilibrium of a polymatrix game is \PPAD-hard. Our main result is that a $(0.5+\delta)$-Nash equilibrium of an $n$-player polymatrix game can be computed in time polynomial in the input size and $\frac{1}{\delta}$. Inspired by the algorithm of Tsaknakis and Spirakis, our algorithm uses gradient descent on the maximum regret of the players. We also show that this algorithm can be applied to efficiently find a $(0.5+\delta)$-Nash equilibrium in a two-player Bayesian game.

Connected through hyperedge HE-1 (induced by source paper "tree polymatrix games are ppad hard"):
  with members:
    - "computing constrained approximate equilibria in polymatrix games"
    - "approximating nash equilibria in tree polymatrix games"
    - "an empirical study on computing equilibria in polymatrix games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (induced by source paper "graphical one sided markets"):
  with members:
    - "schelling games on graphs"
    - "swap stability in schelling games on graphs"
    - "maximin share allocations on cycles"
    - "schelling segregation with strategic agents"
    - "fair allocation of indivisible items with externalities"
    - "almost envy free allocations with connected bundles"
    - "fair division of a graph"
    - "fair division via social comparison"

Connected through hyperedge HE-3 (induced by source paper "convergence of gradient methods on bilinear zero sum games"):
  with members:
    - "training gans with centripetal acceleration"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"

Connected through hyperedge HE-4 (induced by source paper "computing constrained approximate equilibria in polymatrix games"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "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"
    - "approximate well supported nash equilibria below two thirds"

Connected through hyperedge HE-5 (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-6 (induced by source paper "an empirical study on computing equilibria in polymatrix games"):
  with members:
    - "approximating nash equilibria in tree polymatrix games"
    - "from duels to battefields computing equilibria of blotto and other games"
    - "an empirical study of finding approximate equilibria in bimatrix games"
    - "coordination games on graphs"
    - "on the complexity of computing an equilibrium in combinatorial auctions"
    - "inapproximability of nash equilibrium"
    - "computing nash equilibria of action graph games"
    - "the complexity of non monotone markets"

Connected through hyperedge HE-7 (induced by source paper "approximating nash equilibria in tree polymatrix games"):
  with members:
    - "inapproximability of nash equilibrium"
    - "graphical models for game theory"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "on equilibrium computation in biased games with quadratic penalties"):
  with members:
    - "distributed methods for computing approximate equilibria"
    - "the complexity of equilibria for risk modeling valuations"
    - "on the complexity of nash equilibria in anonymous games"
    - "settling the complexity of computing two player nash equilibria"

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)