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: "learning in games robustness of fast convergence"
  Abstract: We show that learning algorithms satisfying a $\textit{low approximate regret}$ property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a $(1+\epsilon)$-multiplicative approximation to the best action in hindsight, is ubiquitous among learning algorithms; it is satisfied even by the vanilla Hedge forecaster. Our results improve upon recent work of Syrgkanis et al. [SALS15] in a number of ways. We require only that players observe payoffs under other players' realized actions, as opposed to expected payoffs. We further show that convergence occurs with high probability, and show convergence under bandit feedback. Finally, we improve upon the speed of convergence by a factor of $n$, the number of players. Both the scope of settings and the class of algorithms for which our analysis provides fast convergence are considerably broader than in previous work. #R##N#Our framework applies to dynamic population games via a low approximate regret property for shifting experts. Here we strengthen the results of Lykouris et al. [LST16] in two ways: We allow players to select learning algorithms from a larger class, which includes a minor variant of the basic Hedge algorithm, and we increase the maximum churn in players for which approximate optimality is achieved. #R##N#In the bandit setting we present a new algorithm which provides a "small loss"-type bound with improved dependence on the number of actions in utility settings, and is both simple and efficient. This result may be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "bandits with adversarial scaling"):
  with members:
    - "beating stochastic and adversarial semi bandits optimally and simultaneously"
    - "improved path length regret bounds for bandits"
    - "competing bandits the perils of exploration under competition"
    - "factored bandits"
    - "stochastic bandits robust to adversarial corruptions"
    - "more adaptive algorithms for adversarial bandits"
    - "make the minority great again first order regret bound for contextual bandits"
    - "bandit regret scaling with the effective loss range"

Connected through hyperedge HE-2 (induced by source paper "a closer look at small loss bounds for bandits with graph feedback"):
  with members:
    - "equipping experts bandits with long term memory"
    - "first order regret analysis of thompson sampling"
    - "bandits with feedback graphs and switching costs"
    - "improved path length regret bounds for bandits"
    - "online learning with feedback graphs and switching costs"
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with graph structured feedback against adaptive adversaries"
    - "small loss bounds for online learning with partial information"

Connected through hyperedge HE-3 (induced by source paper "no regret learning in cournot games"):
  with members:
    - "introduction to online convex optimization"
    - "finding friend and foe in multi agent games"
    - "bandit learning in concave n person games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "fast convergence of regularized learning in games"
    - "on the role of a market maker in networked cournot competition"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (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"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"

Connected through hyperedge HE-5 (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"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"

Connected through hyperedge HE-6 (induced by source paper "the route to chaos in routing games population increase drives period doubling instability chaos inefficiency with price of anarchy equal to one"):
  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"
    - "cooperation speeds surfing use co bandit"
    - "cycles in adversarial regularized learning"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "on the price of anarchy of highly congested nonatomic network games"
    - "the price of anarchy in large games"

Connected through hyperedge HE-7 (induced by source paper "when smoothness is not enough toward exact quantification and optimization of the price of anarchy"):
  with members:
    - "optimal price of anarchy in cost sharing games"
    - "distributed resource allocation through utility design part i optimizing the performance certificates via the price of anarchy"
    - "utility design for distributed resource allocation part i characterizing and optimizing the exact price of anarchy"
    - "the impact of local information on the performance of multiagent systems"
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "the price of anarchy in auctions"
    - "bounding the inefficiency of outcomes in generalized second price auctions"
    - "composable and efficient mechanisms"

Connected through hyperedge HE-8 (induced by source paper "optimal mechanisms for distributed resource allocation"):
  with members:
    - "tight approximation bounds for maximum multi coverage"
    - "optimal price of anarchy in cost sharing games"
    - "distributed resource allocation through utility design part i optimizing the performance certificates via the price of anarchy"
    - "intense competition can drive selfish explorers to optimize coverage"
    - "distributed resource allocation through utility design part ii applications to submodular supermodular and set covering problems"
    - "multiagent maximum coverage problems the trade off between anarchy and stability"
    - "a market inspired approach for intersection management in urban road traffic networks"
    - "a unifying tool for bounding the quality of non cooperative solutions in weighted congestion games"

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)