human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "thompson sampling for combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight lower bounds for combinatorial multi armed bandits")
  Member "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "batch size independent regret bounds for the combinatorial multi armed bandit problem")
  Member "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "thompson sampling for the mnl bandit":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")
  Member "multiple play bandits in the position based model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unbiased learning to rank counterfactual and online approaches")
  Member "dcm bandits learning to rank with multiple clicks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator")
  Member "cascading bandits for large scale recommendation problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting")
  Member "combinatorial cascading bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "combinatorial semi bandit in the non stationary environment")

Question: Please tell me which class the source paper 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)