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 "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"):
  Abstract: We show for the first time, to our knowledge, that it is possible to reconcile in online learning in zero-sum games two seemingly contradictory objectives: vanishing time-average regret and non-vanishing step sizes. This phenomenon, that we coin ``fast and furious" learning in games, sets a new benchmark about what is possible both in max-min optimization as well as in multi-agent systems. Our analysis does not depend on introducing a carefully tailored dynamic. Instead we focus on the most well studied online dynamic, gradient descent. Similarly, we focus on the simplest textbook class of games, two-agent two-strategy zero-sum games, such as Matching Pennies. Even for this simplest of benchmarks the best known bound for total regret, prior to our work, was the trivial one of $O(T)$, which is immediately applicable even to a non-learning agent. Based on a tight understanding of the geometry of the non-equilibrating trajectories in the dual space we prove a regret bound of $\Theta(\sqrt{T})$ matching the well known optimal bound for adaptive step sizes in the online setting. This guarantee holds for all fixed step-sizes without having to know the time horizon in advance and adapt the fixed step-size accordingly. As a corollary, we establish that even with fixed learning rates the time-average of mixed strategies, utilities converge to their exact Nash equilibrium values.

Includes members:
  Member "alpha rank multi agent evaluation by evolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural replicator dynamics")
  Member "multi agent learning in network zero sum games is a hamiltonian system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic")
  Member "open ended learning in symmetric zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  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 "re evaluating evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arena a general evaluation platform and building toolkit for multi agent intelligence")
  Member "learning dynamics and the co evolution of competing sexual species":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic")
  Member "cycles in adversarial regularized learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")

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)