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: "fast rates for bandit optimization with upper confidence frank wolfe"
  Abstract: We consider the problem of bandit optimization, inspired by stochastic optimization and online learning problems with bandit feedback. In this problem, the objective is to minimize a global loss function of all the actions, not necessarily a cumulative loss. This framework allows us to study a very general class of problems, with applications in statistics, machine learning, and other fields. To solve this problem, we analyze the Upper-Confidence Frank-Wolfe algorithm, inspired by techniques for bandits and convex optimization. We give theoretical guarantees for the performance of this algorithm over various classes of functions, and discuss the optimality of these results.

Connected through hyperedge HE-1 (induced by source paper "selfish robustness and equilibria in multi player bandits"):
  with members:
    - "an optimal algorithm in multiplayer multi armed bandits"
    - "competing bandits in matching markets"
    - "introduction to multi armed bandits"
    - "multiplayer bandits without observing collision information"
    - "dynamic pricing with finitely many unknown valuations"
    - "sic mmab synchronisation involves communication in multiplayer multi armed bandits"
    - "multi user communication networks a coordinated multi armed bandit approach"
    - "multi player bandits a musical chairs approach"

Connected through hyperedge HE-2 (induced by source paper "exploration exploitation trade off in reinforcement learning on online markov decision processes with global concave rewards"):
  with members:
    - "provably efficient maximum entropy exploration"
    - "regret bounds for reinforcement learning via markov chain concentration"
    - "adversarial bandits with knapsacks"
    - "multi objective bandits optimizing the generalized gini index"
    - "linear contextual bandits with knapsacks"
    - "an efficient algorithm for contextual bandits with knapsacks and an extension to concave objectives"
    - "bandits with concave rewards and convex knapsacks"
    - "resourceful contextual bandits"

Connected through hyperedge HE-3 (induced by source paper "dynamic pricing with finitely many unknown valuations"):
  with members:
    - "online learning in repeated auctions"
    - "bandits with concave rewards and convex knapsacks"
    - "perfect bayesian equilibria in repeated sales"
    - "bandits with knapsacks"
    - "learning prices for repeated auctions with strategic buyers"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "dynamic pricing with limited supply"

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.LG(Machine Learning)