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: "tight bounds for bandit combinatorial optimization"
  Abstract: We revisit the study of optimal regret rates in bandit combinatorial optimization---a fundamental framework for sequential decision making under uncertainty that abstracts numerous combinatorial prediction problems. We prove that the attainable regret in this setting grows as $\widetilde{\Theta}(k^{3/2}\sqrt{dT})$ where $d$ is the dimension of the problem and $k$ is a bound over the maximal instantaneous loss, disproving a conjecture of Audibert, Bubeck, and Lugosi (2013) who argued that the optimal rate should be of the form $\widetilde{\Theta}(k\sqrt{dT})$. Our bounds apply to several important instances of the framework, and in particular, imply a tight bound for the well-studied bandit shortest path problem. By that, we also resolve an open problem posed by Cesa-Bianchi and Lugosi (2012).

Connected through hyperedge HE-1 (induced by source paper "top k combinatorial bandits with full bandit feedback"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "polynomial time algorithms for combinatorial pure exploration with full bandit feedback"
    - "introduction to multi armed bandits"
    - "combinatorial multi armed bandit with general reward functions"
    - "combinatorial bandits revisited"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "regret in online combinatorial optimization"

Connected through hyperedge HE-2 (induced by source paper "combinatorial bandits with full bandit feedback sample complexity and regret minimization"):
  with members:
    - "polynomial time algorithms for combinatorial pure exploration with full bandit feedback"
    - "introduction to multi armed bandits"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "combinatorial multi armed bandit with general reward functions"

Connected through hyperedge HE-3 (induced by source paper "learning to route efficiently with end to end feedback the value of networked structure"):
  with members:
    - "introduction to multi armed bandits"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "stochastic online shortest path routing the value of feedback"
    - "learning to optimize via posterior sampling"
    - "volumetric spanners an efficient exploration basis for learning"
    - "adaptive shortest path routing under unknown and stochastically varying link states"
    - "thompson sampling for contextual bandits with linear payoffs"

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)