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: "empirical distribution of equilibrium play and its testing application"
  Abstract: We show that in any $n$-player $m$-action normal-form game, we can obtain an approximate equilibrium by sampling any mixed-action equilibrium a small number of times. We study three types of equilibria: Nash, correlated and coarse correlated. For each one of them we obtain upper and lower bounds on the number of samples required for the empirical distribution over the sampled action profiles to form an approximate equilibrium with probability close to one. #R##N#These bounds imply that using a small number of samples we can test whether or not players are playing according to an approximate equilibrium, even in games where $n$ and $m$ are large. In addition, our results substantially improve previously known upper bounds on the support size of approximate equilibria in games with many players. In particular, for all the three types of equilibria we show the existence of approximate equilibrium with support size polylogarithmic in $n$ and $m$, whereas the previously best-known upper bounds were polynomial in $n$.

Connected through hyperedge HE-1 (induced by source paper "the sample complexity of up to varepsilon multi dimensional revenue maximization"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "bernoulli factories and black box reductions in mechanism design"
    - "learning multi item auctions with or without samples"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "simple mechanisms for subadditive buyers via duality"
    - "learning simple auctions"
    - "the menu size complexity of revenue approximation"

Connected through hyperedge HE-2 (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"
    - "query complexity of approximate equilibria in anonymous games"
    - "learning equilibria of games via payoff queries"
    - "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-3 (induced by source paper "approximating the existential theory of the reals"):
  with members:
    - "computing constrained approximate equilibria in polymatrix games"
    - "approximating nash equilibria in tree polymatrix games"
    - "lipschitz continuity and approximate equilibria"
    - "inapproximability of np complete variants of nash equilibrium"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "competing prediction algorithms"):
  with members:
    - "competing bandits learning under competition"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-5 (induced by source paper "simple approximate equilibria in games with many players"):
  with members:
    - "settling the complexity of computing approximate two player nash equilibria"

Connected through hyperedge HE-6 (induced by source paper "efficient empirical revenue maximization in single parameter auction environments"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "ironing in the dark"
    - "sampling and representation complexity of revenue maximization"
    - "making the most of your samples"
    - "the menu size complexity of auctions"

Connected through hyperedge HE-7 (induced by source paper "the query complexity of correlated equilibria"):
  with members:
    - "well supported versus approximate nash equilibria query complexity of large games"
    - "learning equilibria of games via payoff queries"
    - "query complexity of approximate nash equilibria"
    - "query complexity of correlated equilibrium"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "simple strategies for large zero sum games with applications to complexity theory"

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)