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 "tight lower bounds for combinatorial multi armed bandits"):
  Abstract: The Combinatorial Multi-Armed Bandit problem is a sequential decision-making problem in which an agent selects a set of arms on each round, observes feedback for each of these arms and aims to maximize a known reward function of the arms it chose. While previous work proved regret upper bounds in this setting for general reward functions, only a few works provided matching lower bounds, all for specific reward functions. In this work, we prove regret lower bounds for combinatorial bandits that hold under mild assumptions for all smooth reward functions. We derive both problem-dependent and problem-independent bounds and show that the recently proposed Gini-weighted smoothness parameter \citep{merlisM19} also determines the lower bounds for monotone reward functions. Notably, this implies that our lower bounds are tight up to log-factors.

Includes members:
  Member "top k combinatorial bandits with full bandit feedback":
    also connected through other hyperedges:
      (none)
  Member "thompson sampling for combinatorial network optimization in unknown environments":
    also connected through other hyperedges:
      (none)
  Member "thompson sampling for combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on thompson sampling for smoother than lipschitz bandits")
  Member "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "nearly instance optimal sample complexity bounds for top k arm selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploratory machine learning with unknown unknowns")
  Member "combinatorial multi armed bandit with general reward functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "combinatorial semi bandit with known covariance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online joint bid daily budget optimization of internet advertising campaigns")
  Member "combinatorial cascading bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)