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: "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.

Connected through hyperedge HE-1 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners"):
  with members:
    - "logan latent optimisation for generative adversarial networks"
    - "a closer look at the optimization landscapes of generative adversarial networks"
    - "differentiable game mechanics"
    - "the mechanics of n player differentiable games"
    - "global convergence to the equilibrium of gans using variational inequalities"
    - "the numerics of gans"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "last iterate is slower than averaged iterate in smooth convex concave saddle point problems"):
  with members:
    - "accelerating smooth games by manipulating spectral shapes"
    - "a tight and unified analysis of extragradient for a whole spectrum of differentiable games"
    - "training gans with centripetal acceleration"
    - "lower bounds and conditioning of differentiable games"
    - "a variational inequality perspective on generative adversarial nets"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "negative momentum for improved game dynamics"
    - "the numerics of gans"

Connected through hyperedge HE-3 (induced by source paper "combining no regret and q learning"):
  with members:
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "stable predictive optimistic counterfactual regret minimization"
    - "learning to collaborate in markov decision processes"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "regret circuits composability of regret minimizers"
    - "solving imperfect information games via discounted regret minimization"
    - "deep counterfactual regret minimization"
    - "cycles in adversarial regularized learning"

Connected through hyperedge HE-4 (induced by source paper "convergence of gradient methods on bilinear zero sum games"):
  with members:
    - "training gans with centripetal acceleration"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "negative momentum for improved game dynamics"
    - "which training methods for gans do actually converge"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "the numerics of gans"
    - "cycles in adversarial regularized learning"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-5 (induced by source paper "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "alpha rank multi agent evaluation by evolution"
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "learning dynamics and the co evolution of competing sexual species"
    - "cycles in adversarial regularized learning"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"

Connected through hyperedge HE-6 (induced by source paper "fictitious play convergence smoothness and optimism"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "introduction to online convex optimization"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "on the exponential rate of convergence of fictitious play in potential games"
    - "a counter example to karlin s strong conjecture for fictitious play"
    - "payoff performance of fictitious play"

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)