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: "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
  Abstract: We introduce a new approach to the study of influence in strategic settings where the action of an individual depends on that of others in a network-structured way. We propose network influence games as a game-theoretic model of the behavior of a large but finite networked population. In particular, we study an instance we call linear-influence games that allows both positive and negative influence factors, permitting reversals in behavioral choices. We embrace pure-strategy Nash equilibrium, an important solution concept in non-cooperative game theory, to formally define the stable outcomes of a network influence game and to predict potential outcomes without explicitly considering intricate dynamics. We address an important problem in network influence, the identification of the most influential individuals, and approach it algorithmically using pure-strategy Nash-equilibria computation. Computationally, we provide (a) complexity characterizations of various problems on linear-influence games; (b) efficient algorithms for several special cases and heuristics for hard cases; and (c) approximation algorithms, with provable guarantees, for the problem of identifying the most influential individuals. Experimentally, we evaluate our approach using both synthetic network influence games and real-world settings of general interest, each corresponding to a separate branch of the U.S. Government. Mathematically, we connect linear-influence games to important models in game theory: potential and polymatrix 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"
    - "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 "on the sample complexity of learning graphical games"):
  with members:
    - "from behavior to sparse graphical games efficient recovery of equilibria"
    - "graphical models for game theory"
    - "sparsityboost a new scoring function for learning bayesian network structure"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "polynomial time computation of exact correlated equilibrium in compact games"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-4 (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"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "a continuation method for nash equilibria in structured games"

Connected through hyperedge HE-5 (induced by source paper "learning the structure and parameters of large population graphical games from behavioral data"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "graphical models for game theory"
    - "computational rationalization the inverse equilibrium problem"
    - "learning game representations from data using rationality constraints"
    - "learning and solving many player games through a cluster based representation"
    - "convex structure learning for bayesian networks polynomial feature selection and approximate ordering"
    - "knowledge combination in graphical multiagent model"
    - "polynomial time computation of exact correlated equilibrium in compact 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)