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: "online learning with feedback graphs without the graphs"
  Abstract: We study an online learning framework introduced by Mannor and Shamir (2011) in which the feedback is specified by a graph, in a setting where the graph may vary from round to round and is \emph{never fully revealed} to the learner. We show a large gap between the adversarial and the stochastic cases. In the adversarial case, we prove that even for dense feedback graphs, the learner cannot improve upon a trivial regret bound obtained by ignoring any additional feedback besides her own loss. In contrast, in the stochastic case we give an algorithm that achieves $\widetilde \Theta(\sqrt{\alpha T})$ regret over $T$ rounds, provided that the independence numbers of the hidden feedback graphs are at most $\alpha$. We also extend our results to a more general feedback model, in which the learner does not necessarily observe her own loss, and show that, even in simple cases, concealing the feedback graphs might render a learnable problem unlearnable.

Connected through hyperedge HE-1 (induced by source paper "feedback graph regret bounds for thompson sampling and ucb"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "small loss bounds for online learning with partial information"
    - "reward maximization under uncertainty leveraging side observations on networks"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"

Connected through hyperedge HE-2 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"

Connected through hyperedge HE-3 (induced by source paper "connections between mirror descent thompson sampling and the information ratio"):
  with members:
    - "first order regret analysis of thompson sampling"
    - "introduction to online convex optimization"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "sparsity variance and curvature in multi armed bandits"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "towards optimal algorithms for prediction with expert advice"

Connected through hyperedge HE-4 (induced by source paper "bandits with feedback graphs and switching costs"):
  with members:
    - "online learning with feedback graphs and switching costs"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with abstention"
    - "corralling a band of bandit algorithms"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "bandits with switching costs t 2 3 regret"

Connected through hyperedge HE-5 (induced by source paper "stochastic online learning with probabilistic graph feedback"):
  with members:
    - "cleaning up the neighborhood a full classification for adversarial partial monitoring"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "information directed sampling for stochastic bandits with graph feedback"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "influence maximization with bandits"
    - "online learning with feedback graphs beyond bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

Connected through hyperedge HE-6 (induced by source paper "semi parametric dynamic contextual pricing"):
  with members:
    - "small loss bounds for online learning with partial information"
    - "learning simple auctions"
    - "from bandits to experts a tale of domination and independence"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "leveraging side observations in stochastic bandits"
    - "from bandits to experts on the value of side observations"

Connected through hyperedge HE-7 (induced by source paper "graph regret bounds for thompson sampling and ucb"):
  with members:
    - "information directed sampling for stochastic bandits with graph feedback"
    - "online learning with feedback graphs beyond bandits"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-8 (induced by source paper "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "the sample complexity of revenue maximization"
    - "learning and efficiency in games with dynamic population"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"

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)