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: "multi agent learning in network zero sum games is a hamiltonian system"
  Abstract: Zero-sum games are natural, if informal, analogues of closed physical systems where no energy/utility can enter or exit. This analogy can be extended even further if we consider zero-sum network (polymatrix) games where multiple agents interact in a closed economy. Typically, (network) zero-sum games are studied from the perspective of Nash equilibria. Nevertheless, this comes in contrast with the way we typically think about closed physical systems, e.g., Earth-moon systems which move perpetually along recurrent trajectories of constant energy. #R##N#We establish a formal and robust connection between multi-agent systems and Hamiltonian dynamics -- the same dynamics that describe conservative systems in physics. Specifically, we show that no matter the size, or network structure of such closed economies, even if agents use different online learning dynamics from the standard class of Follow-the-Regularized-Leader, they yield Hamiltonian dynamics. This approach generalizes the known connection to Hamiltonians for the special case of replicator dynamics in two agent zero-sum games developed by Hofbauer. Moreover, our results extend beyond zero-sum settings and provide a type of a Rosetta stone (see e.g. Table 1) that helps to translate results and techniques between online optimization, convex analysis, games theory, and physics.

Connected through hyperedge HE-1 (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"
    - "learning dynamics and the co evolution of competing sexual species"
    - "shrewd selection speeds surfing use smart exp3"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "learning in games robustness of fast convergence"

Connected through hyperedge HE-2 (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"
    - "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"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "training gans with optimism"

Connected through hyperedge HE-3 (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"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "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-4 (induced by source paper "finite regret and cycles with fixed step size via alternating gradient descent ascent"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "the mechanics of n player differentiable games"
    - "optimistic mirror descent in saddle point problems going the extra gradient mile"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"
    - "learning in games robustness of fast convergence"

Connected through hyperedge HE-5 (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"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "introduction to online convex optimization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "generalized potential games"):
  with members:
    - "algorithms and properties for positive symmetrizable matrices"
    - "a comprehensive survey of potential game approaches to wireless networks"

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)