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 combinatorial network optimization in unknown environments"):
  Abstract: Influence maximization, item recommendation, adaptive routing and dynamic spectrum allocation all require choosing the right action from a large set of alternatives. Thanks to the advances in combinatorial optimization, these and many similar problems can be efficiently solved given that the stochasticity of the environment is perfectly known. In this paper, we take this one step further and focus on combinatorial optimization in unknown environments. All of these settings fit into the general combinatorial learning framework called combinatorial multi-armed bandit with probabilistically triggered arms. We consider a very powerful Bayesian algorithm, Combinatorial Thompson Sampling (CTS), and analyze its regret under the semi-bandit feedback model. Assuming that the learner does not know the expected base arm outcomes beforehand but has access to an exact oracle, we show that when the expected reward is Lipschitz continuous in the expected base arm outcomes CTS achieves $O(\sum_{i =1}^m \log T / (p_i \Delta_i))$ regret, where $m$ denotes the number of base arms, $p_i$ denotes the minimum non-zero triggering probability of base arm $i$, $\Delta_i$ denotes the minimum suboptimality gap of base arm $i$ and $T$ denotes the time horizon. In addition, we prove that when triggering probabilities are at least $p^*>0$ for all base arms, CTS achieves $O(1/p^*\log(1/p^*))$ regret independent of the time horizon. We also numerically compare CTS with algorithms that use the principle of optimism in the face of uncertainty in several combinatorial networking problems, and show that CTS outperforms these algorithms by at least an order of magnitude in the majority of the cases.

Includes members:
  Member "batch size independent regret bounds for the combinatorial multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "top k combinatorial bandits with full bandit feedback")
  Member "stochastic online learning with probabilistic graph feedback":
    also connected through other hyperedges:
      (none)
  Member "combinatorial bandits with full bandit feedback sample complexity and regret minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "best arm identification for cascading bandits in the fixed confidence setting")
  Member "thompson sampling for combinatorial semi bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tight lower bounds for combinatorial multi armed bandits")
  Member "bandits with side observations bounded vs logarithmic regret":
    also connected through other hyperedges:
      (none)
  Member "analysis of thompson sampling for combinatorial multi armed bandit with probabilistically triggered arms":
    also connected through other hyperedges:
      (none)
  Member "regret bounds for stochastic combinatorial multi armed bandits with linear space complexity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combinatorial bandits with full bandit feedback sample complexity and regret minimization")
  Member "improving regret bounds for combinatorial semi bandits with probabilistically triggered arms and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)