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: "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
  Abstract: The Multiplicative Weights Update (MWU) method is a ubiquitous meta-algorithm that works as follows: A distribution is maintained on a certain set, and at each step the probability assigned to element $\gamma$ is multiplied by $(1 -\epsilon C(\gamma))>0$ where $C(\gamma)$ is the "cost" of element $\gamma$ and then rescaled to ensure that the new values form a distribution. We analyze MWU in congestion games where agents use \textit{arbitrary admissible constants} as learning rates $\epsilon$ and prove convergence to \textit{exact Nash equilibria}. Our proof leverages a novel connection between MWU and the Baum-Welch algorithm, the standard instantiation of the Expectation-Maximization (EM) algorithm for hidden Markov models (HMM). Interestingly, this convergence result does not carry over to the nearly homologous MWU variant where at each step the probability assigned to element $\gamma$ is multiplied by $(1 -\epsilon)^{C(\gamma)}$ even for the most innocuous case of two-agent, two-strategy load balancing games, where such dynamics can provably lead to limit cycles or even chaotic behavior.

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"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "last iterate convergence in no regret learning constrained min max optimization for convex concave landscapes"):
  with members:
    - "training gans with optimism"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-3 (induced by source paper "a generalized training approach for multiagent learning"):
  with members:
    - "evolutionary reinforcement learning for sample efficient multiagent coordination"
    - "multiagent evaluation under incomplete information"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "openspiel a framework for reinforcement learning in games"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "open ended learning in symmetric zero sum games"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "alpha alpha rank practically scaling alpha rank through stochastic optimisation"):
  with members:
    - "a generalized training approach for multiagent learning"
    - "multiagent evaluation under incomplete information"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "multi agent generalized recursive reasoning"
    - "openspiel a framework for reinforcement learning in games"
    - "bi level actor critic for multi agent coordination"
    - "open ended learning in symmetric zero sum games"

Connected through hyperedge HE-5 (induced by source paper "weighted voting on the blockchain improving consensus in proof of stake protocols"):
  with members:
    - "incentives in ethereum s hybrid casper protocol"
    - "reward sharing schemes for stake pools"
    - "hyperledger fabric a distributed operating system for permissioned blockchains"
    - "rethinking blockchain security position paper"
    - "betting on blockchain consensus with fantomette"
    - "consensus in the age of blockchains"
    - "casper the friendly finality gadget"
    - "untangling blockchain a data processing view of blockchain systems"

Connected through hyperedge HE-6 (induced by source paper "the route to chaos in routing games when is price of anarchy too optimistic"):
  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"
    - "shrewd selection speeds surfing use smart exp3"
    - "cycles in adversarial regularized learning"
    - "learning in games robustness of fast convergence"

Connected through hyperedge HE-7 (induced by source paper "alpha rank multi agent evaluation by evolution"):
  with members:
    - "emergent coordination through competition"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "the exact computational complexity of evolutionarily stable strategies"
    - "learning dynamics and the co evolution of competing sexual species"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"

Connected through hyperedge HE-8 (induced by source paper "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"):
  with members:
    - "alpha rank multi agent evaluation by evolution"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "open ended learning in symmetric zero sum games"
    - "the mechanics of n player differentiable games"
    - "re evaluating evaluation"
    - "learning dynamics and the co evolution of competing sexual species"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"

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)