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: "query complexity of approximate equilibria in anonymous games"
  Abstract: We study the computation of equilibria of anonymous games, via algorithms that may proceed via a sequence of adaptive queries to the game's payoff function, assumed to be unknown initially. The general topic we consider is \emph{query complexity}, that is, how many queries are necessary or sufficient to compute an exact or approximate Nash equilibrium. #R##N#We show that exact equilibria cannot be found via query-efficient algorithms. We also give an example of a 2-strategy, 3-player anonymous game that does not have any exact Nash equilibrium in rational numbers. However, more positive query-complexity bounds are attainable if either further symmetries of the utility functions are assumed or we focus on approximate equilibria. We investigate four sub-classes of anonymous games previously considered by \cite{bfh09, dp14}. #R##N#Our main result is a new randomized query-efficient algorithm that finds a $O(n^{-1/4})$-approximate Nash equilibrium querying $\tilde{O}(n^{3/2})$ payoffs and runs in time $\tilde{O}(n^{3/2})$. This improves on the running time of pre-existing algorithms for approximate equilibria of anonymous games, and is the first one to obtain an inverse polynomial approximation in poly-time. We also show how this can be utilized as an efficient polynomial-time approximation scheme (PTAS). Furthermore, we prove that $\Omega(n \log{n})$ payoffs must be queried in order to find any $\epsilon$-well-supported Nash equilibrium, even by randomized algorithms.

Connected through hyperedge HE-1 (induced by source paper "learning convex partitions and computing game theoretic equilibria from best response queries"):
  with members:
    - "the query complexity of correlated equilibria"
    - "communication complexity of approximate nash equilibria"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "learning equilibria of games via payoff queries"
    - "empirical distribution of equilibrium play and its testing application"
    - "query complexity of approximate nash equilibria"
    - "on the approximation performance of fictitious play in finite games"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-2 (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-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"
    - "on oblivious ptas s for nash equilibrium"
    - "learning poisson binomial distributions"
    - "discretized multinomial distributions and nash equilibria in anonymous games"
    - "computing equilibria in anonymous games"

Connected through hyperedge HE-4 (induced by source paper "logarithmic query complexity for approximate nash computation in large games"):
  with members:
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "mechanism design in large games incentives and privacy"

Connected through hyperedge HE-5 (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-6 (induced by source paper "well supported versus approximate nash equilibria query complexity of large games"):
  with members:
    - "the query complexity of correlated equilibria"
    - "on the structure covering and learning of poisson multinomial distributions"
    - "can almost everybody be almost happy pcp for ppad and the inapproximability of nash"
    - "on the complexity of nash equilibria in anonymous games"
    - "inapproximability of nash equilibrium"
    - "constant rank bimatrix games are ppad hard"
    - "finding approximate nash equilibria of bimatrix games via payoff queries"
    - "learning equilibria of games via payoff queries"

Connected through hyperedge HE-7 (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"
    - "new algorithms and lower bounds for monotonicity testing"
    - "optimal hitting sets for combinatorial shapes"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of nash equilibria in anonymous games"):
  with members:
    - "inapproximability of nash equilibrium"
    - "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"
    - "discretized multinomial distributions and nash equilibria in anonymous games"
    - "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)