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: "combinatorial multi armed bandit with probabilistically triggered arms a case with bounded regret"
  Abstract: In this paper, we study the combinatorial multi-armed bandit problem (CMAB) with probabilistically triggered arms (PTAs). Under the assumption that the arm triggering probabilities (ATPs) are positive for all arms, we prove that a class of upper confidence bound (UCB) policies, named Combinatorial UCB with exploration rate $\kappa$ (CUCB-$\kappa$), and Combinatorial Thompson Sampling (CTS), which estimates the expected states of the arms via Thompson sampling, achieve bounded regret. In addition, we prove that CUCB-$0$ and CTS incur $O(\sqrt{T})$ gap-independent regret. These results improve the results in previous works, which show $O(\log T)$ gap-dependent and $O(\sqrt{T\log T})$ gap-independent regrets, respectively, under no assumptions on the ATPs. Then, we numerically evaluate the performance of CUCB-$\kappa$ and CTS in a real-world movie recommendation problem, where the actions correspond to recommending a set of movies, the arms correspond to the edges between the movies and the users, and the goal is to maximize the total number of users that are attracted by at least one movie. Our numerical results complement our theoretical findings on bounded regret. Apart from this problem, our results also directly apply to the online influence maximization (OIM) problem studied in numerous prior works.

Connected through hyperedge HE-1 (induced by source paper "thompson sampling for combinatorial network optimization in unknown environments"):
  with members:
    - "batch size independent regret bounds for the combinatorial multi armed bandit problem"
    - "stochastic online learning with probabilistic graph feedback"
    - "combinatorial bandits with full bandit feedback sample complexity and regret minimization"
    - "thompson sampling for combinatorial semi bandits"
    - "bandits with side observations bounded vs logarithmic regret"
    - "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"
    - "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"

Connected through hyperedge HE-2 (induced by source paper "thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms"):
  with members:
    - "thompson sampling for combinatorial semi bandits"
    - "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"
    - "cascading bandits learning to rank in the cascade model"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "matroid bandits fast combinatorial optimization with learning"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "learning to optimize via posterior sampling"
    - "analysis of thompson sampling for the multi armed bandit problem"

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)