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 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.

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 algorithms 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 "thompson sampling for a fatigue aware online recommendation system"):
  with members:
    - "dynamic learning of sequential choice bandit problem under marketing fatigue"
    - "thompson sampling for combinatorial semi bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "mnl bandit a dynamic learning approach to assortment selection"
    - "a tutorial on thompson sampling"
    - "thompson sampling for the mnl bandit"
    - "combinatorial multi armed bandit with general reward functions"
    - "cascading bandits learning to rank in the cascade model"

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)