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: "a continuation method for nash equilibria in structured games"
  Abstract: Structured game representations have recently attracted interest as models for multi-agent artificial intelligence scenarios, with rational behavior most commonly characterized by Nash equilibria. This paper presents efficient, exact algorithms for computing Nash equilibria in structured game representations, including both graphical games and multi-agent influence diagrams (MAIDs). The algorithms are derived from a continuation method for normal-form and extensive-form games due to Govindan and Wilson; they follow a trajectory through a space of perturbed games and their equilibria, exploiting game structure through fast computation of the Jacobian of the payoff function. They are theoretically guaranteed to find at least one equilibrium of the game, and may find more. Our approach provides the first efficient algorithm for computing exact equilibria in graphical games with arbitrary topology, and the first algorithm to exploit fine-grained structural properties of MAIDs. Experimental results are presented demonstrating the effectiveness of the algorithms and comparing them to predecessors. The running time of the graphical game algorithm is similar to, and often better than, the running time of previous approximate algorithms. The algorithm for MAIDs can effectively solve games that are much larger than those solvable by previous methods.

Connected through hyperedge HE-1 (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"
    - "learning the structure and parameters of large population graphical games from behavioral data"

Connected through hyperedge HE-2 (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"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-3 (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"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "polynomial time computation of exact correlated equilibrium in compact 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"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"

Connected through hyperedge HE-5 (induced by source paper "learning the structure and parameters of large population graphical games from behavioral data"):
  with members:
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "temporal action graph games a new representation for dynamic games"):
  with members:
    - "computational analysis of perfect information position auctions"
    - "graphical models for game theory"
    - "context specific independence in bayesian networks"
    - "evaluating influence diagrams using limids"
    - "computing nash equilibria of action graph games"
    - "exploiting contextual independence in probabilistic inference"

Connected through hyperedge HE-7 (induced by source paper "exploiting structure in cooperative bayesian games"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "map estimation message passing and perfect graphs"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-8 (induced by source paper "computing nash equilibria of action graph games"):
  with members:
    - "graphical models for game theory"
    - "efficient nash computation in large population games with bounded influence"

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)