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 in time varying games"
  Abstract: In this paper, we examine the long-term behavior of regret-minimizing agents in time-varying games with continuous action spaces. In its most basic form, (external) regret minimization guarantees that an agent's cumulative payoff is no worse in the long run than that of the agent's best fixed action in hindsight. Going beyond this worst-case guarantee, we consider a dynamic regret variant that compares the agent's accrued rewards to those of any sequence of play. Specializing to a wide class of no-regret strategies based on mirror descent, we derive explicit rates of regret minimization relying only on imperfect gradient obvservations. We then leverage these results to show that players are able to stay close to Nash equilibrium in time-varying monotone games - and even converge to Nash equilibrium if the sequence of stage games admits a limit.

Connected through hyperedge HE-1 (induced by source paper "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "bandit learning in concave n person games"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "delay and cooperation in nonstochastic bandits"
    - "learning and efficiency in games with dynamic population"

Connected through hyperedge HE-2 (induced by source paper "memory constrained no regret learning in adversarial bandits"):
  with members:
    - "regret minimisation in multi armed bandits using bounded arm memory"
    - "stochastic multi armed bandits in constant space"
    - "learning and efficiency in games with dynamic population"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "pricing mechanism for resource sustainability in competitive online learning multi agent systems"):
  with members:
    - "online convex optimization and no regret learning algorithms guarantees and applications"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "trading regret for efficiency online convex optimization with long term constraints"

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)