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 "finite regret and cycles with fixed step size via alternating gradient descent ascent"):
  Abstract: Gradient descent is arguably one of the most popular online optimization methods with a wide array of applications. However, the standard implementation where agents simultaneously update their strategies yields several undesirable properties; strategies diverge away from equilibrium and regret grows over time. In this paper, we eliminate these negative properties by introducing a different implementation to obtain finite regret via arbitrary fixed step-size. We obtain this surprising property by having agents take turns when updating their strategies. In this setting, we show that an agent that uses gradient descent obtains bounded regret -- regardless of how their opponent updates their strategies. Furthermore, we show that in adversarial settings that agents' strategies are bounded and cycle when both are using the alternating gradient descent algorithm.

Includes members:
  Member "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic")
  Member "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combining no regret and q learning")
  Member "multi agent learning in network zero sum games is a hamiltonian system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic")
  Member "the mechanics of n player differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")
  Member "optimistic mirror descent in saddle point problems going the extra gradient mile":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimality and stability in non convex non concave min max optimization")
  Member "cycles in adversarial regularized learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "training gans with optimism":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gans may have no nash equilibria")
  Member "learning in games robustness of fast convergence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bandits with adversarial scaling")

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)