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: "discretized multinomial distributions and nash equilibria in anonymous games"
  Abstract: We show that there is a polynomial-time approximation scheme for computing Nash equilibria in anonymous games with any fixed number of strategies (a very broad and important class of games), extending the two-strategy result of Daskalakis and Papadimitriou 2007. The approximation guarantee follows from a probabilistic result of more general interest: The distribution of the sum of n independent unit vectors with values ranging over {e1,...,ek}, where ei is the unit vector along dimension i of the k-dimensional Euclidean space, can be approximated by the distribution of the sum of another set of independent unit vectors whose probabilities of obtaining each value are multiples of 1/z for some integer z, and so that the variational distance of the two distributions is at most eps, where eps is bounded by an inverse polynomial in z and a function of k, but with no dependence on n. Our probabilistic result specifies the construction of a surprisingly sparse epsi-cover- under the total variation distance - of the set of distributions of sums of independent unit vectors, which is of interest on its own right.

Connected through hyperedge HE-1 (induced by source paper "private hypothesis selection"):
  with members:
    - "the optimal approximation factor in density estimation"
    - "differentially private algorithms for learning mixtures of separated gaussians"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "privately learning high dimensional distributions"
    - "locally private gaussian estimation"
    - "private pac learning implies finite littlestone dimension"
    - "learning sums of independent random variables with sparse collective support"
    - "inspectre privately estimating the unseen"

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"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-4 (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-5 (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"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-6 (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"

Connected through hyperedge HE-7 (induced by source paper "privacy and truthful equilibrium selection for aggregative games"):
  with members:
    - "private matchings and allocations"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "privately solving linear programs"
    - "on the complexity of nash equilibria in anonymous games"
    - "privacy preserving public information for sequential games"
    - "approximately stable school optimal and student truthful many to one matchings via differential privacy"
    - "asymptotically truthful equilibrium selection in large congestion games"
    - "privacy and mechanism design"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of nash equilibria in anonymous games"):
  with members:
    - "inapproximability of nash equilibrium"
    - "query complexity of approximate equilibria in anonymous games"
    - "constant rank bimatrix games are ppad hard"
    - "the complexity of non monotone markets"
    - "efficient computation of approximate pure nash equilibria in congestion games"
    - "approximate pure nash equilibria in weighted congestion games existence efficient computation and structure"
    - "on oblivious ptas s for nash equilibrium"
    - "an efficient ptas for two strategy anonymous games"

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)