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 "learning quadratic games on networks"):
  Abstract: Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. In the economics literature, such strategic interactions are often modeled as games played on networks, where an individual's payoff depends not only on her action but also that of her neighbors. The current literature has largely focused on analyzing the characteristics of network games in the scenario where the structure of the network, which is represented by a graph, is known beforehand. It is often the case, however, that the actions of the players are readily observable while the underlying interaction network remains hidden. In this paper, we propose two novel frameworks for learning, from the observations on individual actions, network games with linear-quadratic payoffs, and in particular the structure of the interaction network. Our frameworks are based on the Nash equilibrium of such games and involve solving a joint optimization problem for the graph structure and the individual marginal benefits. We test the proposed frameworks in synthetic settings and further study several factors that affect their learning performance. Moreover, with experiments on three real world examples, we show that our methods can effectively and more accurately learn the games than the baselines. The proposed approach is among the first of its kind for learning quadratic games, and have both theoretical and practical implications for understanding strategic interactions in a network environment.

Includes members:
  Member "learning graphs from data a signal representation perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "learning graphical games from behavioral data sufficient and necessary conditions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strategic prediction with latent aggregative games")
  Member "targeting interventions in networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games")
  Member "learning sparse potential games in polynomial time and sample complexity":
    also connected through other hyperedges:
      (none)
  Member "active sensing of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")
  Member "graphical models for game theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complexity theory game theory and economics the barbados lectures")
  Member "learning the structure and parameters of large population graphical games from behavioral data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "strategic prediction with latent aggregative 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)