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: "information directed sampling for stochastic bandits with graph feedback"
  Abstract: We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action. We allow the graph structure to vary with time and consider both deterministic and Erd\H{o}s-R\'enyi random graph models. For such a graph feedback model, we first present a novel analysis of Thompson sampling that leads to tighter performance bound than existing work. Next, we propose new Information Directed Sampling based policies that are graph-aware in their decision making. Under the deterministic graph case, we establish a Bayesian regret bound for the proposed policies that scales with the clique cover number of the graph instead of the number of actions. Under the random graph case, we provide a Bayesian regret bound for the proposed policies that scales with the ratio of the number of actions over the expected number of observations per iteration. To the best of our knowledge, this is the first analytical result for stochastic bandits with random graph feedback. Finally, using numerical evaluations, we demonstrate that our proposed IDS policies outperform existing approaches, including adaptions of upper confidence bound, $\epsilon$-greedy and Exp3 algorithms.

Connected through hyperedge HE-1 (induced by source paper "feedback graph regret bounds for thompson sampling and ucb"):
  with members:
    - "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 without the graphs"
    - "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 "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-3 (induced by source paper "bandits with feedback graphs and switching costs"):
  with members:
    - "online learning with feedback graphs and switching costs"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "online learning with abstention"
    - "corralling a band of bandit algorithms"
    - "online learning with feedback graphs without the graphs"
    - "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-4 (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"
    - "online learning with feedback graphs without the graphs"
    - "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-5 (induced by source paper "graph regret bounds for thompson sampling and ucb"):
  with members:
    - "online learning with feedback graphs without the graphs"
    - "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-6 (induced by source paper "satisficing in time sensitive bandit learning"):
  with members:
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "thompson sampling for stochastic bandits with graph feedback"
    - "a tutorial on thompson sampling"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "linear bandits in high dimension and recommendation systems"
    - "further optimal regret bounds for thompson sampling"

Connected through hyperedge HE-7 (induced by source paper "ucboost a boosting approach to tame complexity and optimality for stochastic bandits"):
  with members:
    - "reward maximization under uncertainty leveraging side observations on networks"
    - "a contextual bandit approach to personalized news article recommendation"

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)