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: "playing anonymous games using simple strategies"
  Abstract: We investigate the complexity of computing approximate Nash equilibria in anonymous games. Our main algorithmic result is the following: For any $n$-player anonymous game with a bounded number of strategies and any constant $\delta>0$, an $O(1/n^{1-\delta})$-approximate Nash equilibrium can be computed in polynomial time. Complementing this positive result, we show that if there exists any constant $\delta>0$ such that an $O(1/n^{1+\delta})$-approximate equilibrium can be computed in polynomial time, then there is a fully polynomial-time approximation scheme for this problem. #R##N#We also present a faster algorithm that, for any $n$-player $k$-strategy anonymous game, runs in time $\tilde O((n+k) k n^k)$ and computes an $\tilde O(n^{-1/3} k^{11/3})$-approximate equilibrium. This algorithm follows from the existence of simple approximate equilibria of anonymous games, where each player plays one strategy with probability $1-\delta$, for some small $\delta$, and plays uniformly at random with probability $\delta$. #R##N#Our approach exploits the connection between Nash equilibria in anonymous games and Poisson multinomial distributions (PMDs). Specifically, we prove a new probabilistic lemma establishing the following: Two PMDs, with large variance in each direction, whose first few moments are approximately matching are close in total variation distance. Our structural result strengthens previous work by providing a smooth tradeoff between the variance bound and the number of matching moments.

Connected through hyperedge HE-1 (induced by source paper "learning sums of independent random variables with sparse collective support"):
  with members:
    - "boolean function analysis meets stochastic optimization an approximation scheme for stochastic knapsack"
    - "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"
    - "big data on the rise testing monotonicity of distributions"
    - "learning arbitrary statistical mixtures of discrete distributions"
    - "properly learning poisson binomial distributions in almost polynomial time"

Connected through hyperedge HE-2 (induced by source paper "fourier based testing for families of distributions"):
  with members:
    - "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"
    - "testing shape restrictions of discrete distributions"

Connected through hyperedge HE-3 (induced by source paper "query complexity of approximate equilibria in anonymous games"):
  with members:
    - "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"
    - "on the complexity of nash equilibria in 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)