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: "an empirical study on computing equilibria in polymatrix games"
  Abstract: The Nash equilibrium is an important benchmark for behaviour in systems of strategic autonomous agents. Polymatrix games are a succinct and expressive representation of multiplayer games that model pairwise interactions between players. The empirical performance of algorithms to solve these games has received little attention, despite their wide-ranging applications. In this paper we carry out a comprehensive empirical study of two prominent algorithms for computing a sample equilibrium in these games, Lemke's algorithm that computes an exact equilibrium, and a gradient descent method that computes an approximate equilibrium. Our study covers games arising from a number of interesting applications. We find that Lemke's algorithm can compute exact equilibria in relatively large games in a reasonable amount of time. If we are willing to accept (high-quality) approximate equilibria, then we can deal with much larger games using the descent method. We also report on which games are most challenging for each of the algorithms.

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"
    - "computing approximate nash equilibria in polymatrix games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (induced by source paper "nash equilibrium in smoothed polynomial time for network coordination games"):
  with members:
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "local max cut in smoothed polynomial time"
    - "communication complexity of approximate nash equilibria"
    - "inapproximability of nash equilibrium"
    - "constant rank bimatrix games are ppad hard"
    - "query complexity of approximate nash equilibria"
    - "bilinear games polynomial time algorithms for rank based subclasses"
    - "an experimental analysis of lemke howson algorithm"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "an unsupervised game theoretic approach to saliency detection"):
  with members:
    - "deep saliency with encoded low level distance map and high level features"
    - "visual saliency based on multiscale deep features"
    - "salient object detection a benchmark"
    - "the secrets of salient object segmentation"
    - "fully convolutional networks for semantic segmentation"

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)