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 "distributed learning for stochastic generalized nash equilibrium problems"):
  Abstract: This paper examines a stochastic formulation of the generalized Nash equilibrium problem where agents are subject to randomness in the environment of unknown statistical distribution. We focus on fully distributed online learning by agents and employ penalized individual cost functions to deal with coupled constraints. Three stochastic gradient strategies are developed with constant step-sizes. We allow the agents to use heterogeneous step-sizes and show that the penalty solution is able to approach the Nash equilibrium in a stable manner within    $O(\mu _\text{max})$  , for small step-size value    $\mu _\text{max}$   and sufficiently large penalty parameters. The operation of the algorithm is illustrated by considering the network Cournot competition problem.

Includes members:
  Member "diffusion lms over multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed interference cancellation in multi agent scenarios")
  Member "multitask diffusion adaptation over asynchronous networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a multitask diffusion strategy with optimized inter cluster cooperation")
  Member "multitask diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "on the learning behavior of adaptive networks part ii performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "inverse graph learning over optimization networks")
  Member "asynchronous adaptation and learning over networks part i modeling and stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "on the learning behavior of adaptive networks part i transient analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inverse graph learning over optimization networks")
  Member "diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sparsity constrained distributed unmixing of hyperspectral data")
  Member "diffusion strategies outperform consensus strategies for distributed estimation over adaptive networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")

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)