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 equilibria in anonymous games"
  Abstract: We present efficient approximation algorithms for finding Nash equilibria in anonymous games, that is, games in which the players utilities, though different, do not differentiate between other players. Our results pertain to such games with many players but few strategies. We show that any such game has an approximate pure Nash equilibrium, computable in polynomial time, with approximation O(s^2 L), where s is the number of strategies and L is the Lipschitz constant of the utilities. Finally, we show that there is a PTAS for finding an epsilon

Connected through hyperedge HE-1 (induced by source paper "fourier based testing for families of distributions"):
  with members:
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "pseudorandomness via the discrete fourier transform"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"

Connected through hyperedge HE-2 (induced by source paper "a size free clt for poisson multinomials and its applications"):
  with members:
    - "on the structure covering and learning of poisson multinomial distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "on the complexity of nash equilibria in anonymous games"
    - "new algorithms and lower bounds for monotonicity testing"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "a polynomial time algorithm for lossy population recovery"
    - "on oblivious ptas s for nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "playing anonymous games using simple strategies"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "on the complexity of nash equilibria in anonymous games"
    - "query complexity of approximate equilibria in anonymous games"
    - "on oblivious ptas s for nash equilibrium"
    - "learning poisson binomial distributions"
    - "discretized multinomial distributions and nash equilibria in anonymous games"

Connected through hyperedge HE-4 (induced by source paper "properly learning poisson binomial distributions in almost polynomial time"):
  with members:
    - "a nearly optimal and agnostic algorithm for properly learning a mixture of k gaussians for any constant k"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "optimal testing for properties of distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "pseudorandomness via the discrete fourier transform"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "sample optimal density estimation in nearly linear time"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-5 (induced by source paper "optimal learning via the fourier transform for sums of independent integer random variables"):
  with members:
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "sample optimal density estimation in nearly linear time"
    - "on the complexity of nash equilibria in anonymous games"
    - "near optimal density estimation in near linear time using variable width histograms"
    - "efficient density estimation via piecewise polynomial approximation"

Connected through hyperedge HE-6 (induced by source paper "the fourier transform of poisson multinomial distributions and its algorithmic applications"):
  with members:
    - "a size free clt for poisson multinomials and its applications"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "pseudorandomness via the discrete fourier transform"
    - "properly learning poisson binomial distributions in almost polynomial time"
    - "optimal learning via the fourier transform for sums of independent integer random variables"
    - "on the complexity of nash equilibria in anonymous games"
    - "query complexity of approximate equilibria in anonymous games"
    - "new algorithms and lower bounds for monotonicity testing"

Connected through hyperedge HE-7 (induced by source paper "on the structure covering and learning of poisson multinomial distributions"):
  with members:
    - "testing poisson binomial distributions"
    - "faster and sample near optimal algorithms for proper learning mixtures of gaussians"
    - "on oblivious ptas s for nash equilibrium"
    - "learning poisson binomial distributions"
    - "discretized multinomial distributions and nash equilibria in anonymous games"

Connected through hyperedge HE-8 (induced by source paper "query complexity of approximate equilibria in anonymous games"):
  with members:
    - "playing anonymous games using simple strategies"
    - "a size free clt for poisson multinomials and its applications"
    - "the query complexity of correlated equilibria"
    - "settling the complexity of computing approximate two player nash equilibria"
    - "communication complexity of approximate nash equilibria"
    - "the fourier transform of poisson multinomial distributions and its algorithmic applications"
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "optimal learning via the fourier transform for sums of independent integer random variables"

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)