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: "learning graphical games from behavioral data sufficient and necessary conditions"
  Abstract: In this paper we obtain sufficient and necessary conditions on the number of samples required for exact recovery of the pure-strategy Nash equilibria (PSNE) set of a graphical game from noisy observations of joint actions. We consider sparse linear influence games --- a parametric class of graphical games with linear payoffs, and represented by directed graphs of n nodes (players) and in-degree of at most k. We show that one can efficiently recover the PSNE set of a linear influence game with $O(k^2 \log n)$ samples, under very general observation models. On the other hand, we show that $\Omega(k \log n)$ samples are necessary for any procedure to recover the PSNE set from observations of joint actions.

Connected through hyperedge HE-1 (induced by source paper "strategic prediction with latent aggregative games"):
  with members:
    - "the mechanics of n player differentiable games"
    - "learning to speed up structured output prediction"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning with opponent learning awareness"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-2 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games"):
  with members:
    - "learning quadratic games on networks"
    - "targeting interventions in networks"
    - "from behavior to sparse graphical games efficient recovery of equilibria"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "on the complexity of nash equilibria of action graph games"

Connected through hyperedge HE-3 (induced by source paper "learning quadratic games on networks"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "targeting interventions in networks"
    - "learning sparse potential games in polynomial time and sample complexity"
    - "active sensing of social networks"
    - "learning laplacian matrix in smooth graph signal representations"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "uncovering the temporal dynamics of diffusion networks"

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.LG(Machine Learning)