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 the structure and parameters of large population graphical games from behavioral data"
  Abstract: We consider learning, from strictly behavioral data, the structure and parameters of linear influence games (LIGs), a class of parametric graphical games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic inference (CSI): Making inferences from causal interventions on stable behavior in strategic settings. Applications include the identification of the most influential individuals in large (social) networks. Such tasks can also support policy-making analysis. Motivated by the computational work on LIGs, we cast the learning problem as maximum-likelihood estimation (MLE) of a generative model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation uncovers the fundamental interplay between goodness-of-fit and model complexity: good models capture equilibrium behavior within the data while controlling the true number of equilibria, including those unobserved. We provide a generalization bound establishing the sample complexity for MLE in our framework. We propose several algorithms including convex loss minimization (CLM) and sigmoidal approximations. We prove that the number of exact PSNE in LIGs is small, with high probability; thus, CLM is sound. We illustrate our approach on synthetic data and real-world U.S. congressional voting records. We briefly discuss our learning framework's generality and potential applicability to general graphical games.

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 graphical games from behavioral data sufficient and necessary conditions"
    - "learning with opponent learning awareness"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "on structured prediction theory with calibrated convex surrogate losses"

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"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "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"
    - "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"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "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"
    - "uncovering the temporal dynamics of diffusion networks"

Connected through hyperedge HE-4 (induced by source paper "on the sample complexity of learning graphical games"):
  with members:
    - "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"
    - "graphical models for game theory"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-5 (induced by source paper "on the sample complexity of learning sparse graphical games"):
  with members:
    - "graphical models for game theory"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-6 (induced by source paper "from behavior to sparse graphical games efficient recovery of equilibria"):
  with members:
    - "on the sample complexity of learning sparse graphical games"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "graphical models for game theory"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-7 (induced by source paper "on influence stable behavior and the most influential individuals in networks a game theoretic approach"):
  with members:
    - "graphical models for game theory"
    - "power indices of influence games and new centrality measures for social networks"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "competitive contagion in networks"
    - "pure nash equilibria hard and easy 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.LG(Machine Learning)