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 for cascading bandits"):
  Abstract: We design and analyze TS-Cascade, a Thompson sampling algorithm for the cascading bandit problem. In TS-Cascade, Bayesian estimates of the click probability are constructed using a univariate Gaussian; this leads to a more efficient exploration procedure vis-\`a-vis existing UCB-based approaches. We also incorporate the empirical variance of each item's click probability into the Bayesian updates. These two novel features allow us to prove an expected regret bound of the form $\tilde{O}(\sqrt{KLT})$ where $L$ and $K$ are the number of ground items and the number of items in the chosen list respectively and $T\ge L$ is the number of Thompson sampling update steps. This matches the state-of-the-art regret bounds for UCB-based algorithms. More importantly, it is the first theoretical guarantee on a Thompson sampling algorithm for any stochastic combinatorial bandit problem model with partial feedback. Empirical experiments demonstrate superiority of TS-Cascade compared to existing UCB-based procedures in terms of the expected cumulative regret and the time complexity.

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 "a tutorial on thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective diversity in population based reinforcement learning")
  Member "online learning to rank in stochastic click models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "be aware of non stationarity nearly optimal algorithms for piecewise stationary cascading bandits")
  Member "thompson sampling for the mnl bandit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (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")

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)