human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "on the sample complexity of learning graphical games"):
  Abstract: We analyze the sample complexity of learning graphical games from purely behavioral data. We assume that we can only observe the players' joint actions and not their payoffs. We analyze the sufficient and necessary number of samples for the correct recovery of the set of pure-strategy Nash equilibria (PSNE) of the true game. Our analysis focuses on directed graphs with $n$ nodes and at most $k$ parents per node. Sparse graphs correspond to ${k \in O(1)}$ with respect to $n$, while dense graphs correspond to ${k \in O(n)}$. By using VC dimension arguments, we show that if the number of samples is greater than ${O(k n \log^2{n})}$ for sparse graphs or ${O(n^2 \log{n})}$ for dense graphs, then maximum likelihood estimation correctly recovers the PSNE with high probability. By using information-theoretic arguments, we show that if the number of samples is less than ${\Omega(k n \log^2{n})}$ for sparse graphs or ${\Omega(n^2 \log{n})}$ for dense graphs, then any conceivable method fails to recover the PSNE with arbitrary probability.

Includes members:
  Member "from behavior to sparse graphical games efficient recovery of equilibria":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games")
  Member "on influence stable behavior and the most influential individuals in networks a game theoretic approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strategic prediction with latent aggregative games")
  Member "graphical models for game theory":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "sparsityboost a new scoring function for learning bayesian network structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computationally and statistically efficient learning of causal bayes nets using path queries")
  Member "learning the structure and parameters of large population graphical games from behavioral data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "strategic prediction with latent aggregative games")
  Member "polynomial time computation of exact correlated equilibrium in compact games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "a continuation method for nash equilibria in structured games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games")

Question: Please tell me which class the source paper 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)