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 with minimal information in continuous games"
  Abstract: We introduce a stochastic learning process called the dampened gradient approximation process. While learning models have almost exclusively focused on finite games, in this paper we design a learning process for games with continuous action sets. It is payoff-based and thus requires from players no sophistication and no knowledge of the game. We show that despite such limited information, players will converge to Nash in large classes of games. In particular, convergence to a Nash equilibrium which is stable is guaranteed in all games with strategic complements as well as in concave games; convergence to Nash often occurs in all locally ordinal potential games; convergence to a stable Nash occurs with positive probability in all games with isolated equilibria.

Connected through hyperedge HE-1 (induced by source paper "game theoretic optimization via gradient based nikaido isoda function"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "improved training of wasserstein gans"
    - "stabilizing adversarial nets with prediction methods"
    - "gradient descent gan optimization is locally stable"
    - "training gans with optimism"

Connected through hyperedge HE-2 (induced by source paper "bandit learning in concave n person games"):
  with members:
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "distributed stochastic optimization via matrix exponential learning"
    - "fast convergence of regularized learning in games"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)