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: "on the complexity of bandit linear optimization"
  Abstract: We study the attainable regret for online linear optimization problems with bandit feedback, where unlike the full-information setting, the player can only observe its own loss rather than the full loss vector. We show that the price of bandit information in this setting can be as large as $d$, disproving the well-known conjecture that the regret for bandit linear optimization is at most $\sqrt{d}$ times the full-information regret. Surprisingly, this is shown using "trivial" modifications of standard domains, which have no effect in the full-information setting. This and other results we present highlight some interesting differences between full-information and bandit learning, which were not considered in previous literature.

Connected through hyperedge HE-1 (induced by source paper "a general framework to analyze stochastic linear bandit"):
  with members:
    - "a tutorial on thompson sampling"
    - "the pareto regret frontier for bandits"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-2 (induced by source paper "bandit principal component analysis"):
  with members:
    - "bilinear bandits with low rank structure"
    - "introduction to online convex optimization"
    - "more adaptive algorithms for adversarial bandits"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "sparsity variance and curvature in multi armed bandits"
    - "stochastic low rank bandits"
    - "corralling a band of bandit algorithms"

Connected through hyperedge HE-3 (induced by source paper "tight bounds for bandit combinatorial optimization"):
  with members:
    - "first order regret bounds for combinatorial semi bandits"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "regret in online combinatorial optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "the on line shortest path problem under partial monitoring"

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)