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: "thompson sampling for stochastic bandits with graph feedback"
  Abstract: We present a novel extension of Thompson Sampling for stochastic sequential decision problems with graph feedback, even when the graph structure itself is unknown and/or changing. We provide theoretical guarantees on the Bayesian regret of the algorithm, linking its performance to the underlying properties of the graph. Thompson Sampling has the advantage of being applicable without the need to construct complicated upper confidence bounds for different problems. We illustrate its performance through extensive experimental results on real and simulated networks with graph feedback. More specifically, we tested our algorithms on power law, planted partitions and Erdo's-Renyi graphs, as well as on graphs derived from Facebook and Flixster data. These all show that our algorithms clearly outperform related methods that employ upper confidence bounds, even if the latter use more information about the graph.

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"
    - "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 "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"
    - "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-3 (induced by source paper "automatic ensemble learning for online influence maximization"):
  with members:
    - "model independent online learning for influence maximization"
    - "online influence maximization under independent cascade model with semi bandit feedback"
    - "online influence maximization"
    - "influence maximization with bandits"
    - "influence maximization near optimal time complexity meets practical efficiency"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "an information theoretic analysis of thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-4 (induced by source paper "first order bayesian regret analysis of thompson sampling"):
  with members:
    - "feedback graph regret bounds for thompson sampling and ucb"
    - "an information theoretic approach to minimax regret in partial monitoring"
    - "make the minority great again first order regret bound for contextual bandits"
    - "small loss bounds for online learning with partial information"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "online learning with feedback graphs beyond bandits"
    - "an information theoretic analysis of thompson sampling"
    - "regret in online combinatorial optimization"

Connected through hyperedge HE-5 (induced by source paper "almost boltzmann exploration"):
  with members:
    - "boltzmann exploration done right"
    - "exploring trust aware neighbourhood in trust based recommendation"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "leveraging side observations in stochastic bandits"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "from bandits to experts on the value of side observations"

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"
    - "information directed 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 "small loss bounds for online learning with partial information"):
  with members:
    - "make the minority great again first order regret bound for contextual bandits"
    - "online learning with feedback graphs without the graphs"
    - "learning in games robustness of fast convergence"
    - "bistro an efficient relaxation based method for contextual bandits"
    - "efficient algorithms for adversarial contextual learning"
    - "learning and efficiency in games with dynamic population"
    - "strongly adaptive online learning"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-8 (induced by source paper "information directed sampling for stochastic bandits with graph feedback"):
  with members:
    - "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"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "learning to optimize via information directed sampling"
    - "an information theoretic analysis of thompson sampling"
    - "from bandits to experts a tale of domination and independence"

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)