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 "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications"):
  Abstract: We study combinatorial multi-armed bandit with probabilistically triggered arms (CMAB-T) and semi-bandit feedback. We resolve a serious issue in the prior CMAB-T studies where the regret bounds contain a possibly exponentially large factor of $1/p^*$, where $p^*$ is the minimum positive probability that an arm is triggered by any action. We address this issue by introducing a triggering probability modulated (TPM) bounded smoothness condition into the general CMAB-T framework, and show that many applications such as influence maximization bandit and combinatorial cascading bandit satisfy this TPM condition. As a result, we completely remove the factor of $1/p^*$ from the regret bounds, achieving significantly better regret bounds for influence maximization and cascading bandits than before. Finally, we provide lower bound results showing that the factor $1/p^*$ is unavoidable for general CMAB-T problems, suggesting that the TPM condition is crucial in removing this factor.

Includes members:
  Member "combinatorial multi armed bandit with general reward functions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "multiple play bandits in the position based model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unbiased learning to rank counterfactual and online approaches")
  Member "cascading bandits learning to rank in the cascade model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "tight regret bounds for stochastic combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "matroid bandits fast combinatorial optimization with learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "combinatorial multi armed bandit and its extension to probabilistically triggered arms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "regret analysis of stochastic and nonstochastic multi armed bandit problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")

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)