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 algorithms for cascading bandits"
  Abstract: Motivated by efficient optimization for online recommender systems, we revisit the cascading bandit model proposed by Kveton et al.(2015). While Thompson sampling (TS) algorithms have been shown to be empirically superior to Upper Confidence Bound (UCB) algorithms for cascading bandits, theoretical guarantees are only known for the latter, not the former. In this paper, we close the gap by designing and analyzing a TS algorithm, TS-Cascade, that achieves the state-of-the-art regret bound for cascading bandits. In complement, we derive a nearly matching regret lower bound, with information-theoretic techniques and judiciously constructed cascading bandit instances. Finally, we consider a linear generalization of the cascading bandit model, which allows efficient learning in large cascading bandit problem instances. We introduce a TS algorithm, which enjoys a regret bound that depends on the dimension of the linear model but not the number of items. Our paper establishes the first theoretical guarantees on TS algorithms for stochastic combinatorial bandit problem model with partial feedback. Numerical experiments demonstrate the superiority of our TS algorithms compared to existing UCB alogrithms.

Connected through hyperedge HE-1 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting"):
  with members:
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for cascading bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "cascading bandits for large scale recommendation problems"
    - "combinatorial cascading bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "a variant of azuma s inequality for martingales with subgaussian tails"

Connected through hyperedge HE-2 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits"):
  with members:
    - "a near optimal change detection based algorithm for piecewise stationary combinatorial semi bandits"
    - "cascading non stationary bandits online learning to rank in the non stationary cascade model"
    - "bandit online learning with unknown delays"
    - "a change detection based framework for piecewise stationary multi armed bandit problem"
    - "online learning to rank in stochastic click models"
    - "combinatorial bandits revisited"
    - "cascading bandits learning to rank in the cascade model"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"

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)