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 "constrained thompson sampling for wireless link optimization"):
  Abstract: Wireless communication systems operate in complex time-varying environments. Therefore, selecting the optimal configuration parameters in these systems is a challenging problem. For wireless links, rate selection is used to select the optimal data transmission rate that maximizes the link throughput subject to an application-defined latency constraint. We model rate selection as a stochastic multi-armed bandit (MAB) problem, where a finite set of transmission rates are modeled as independent bandit arms. For this setup, we propose Con-TS, a novel constrained version of the Thompson sampling algorithm, where the latency requirement is modeled by a linear constraint on arm selection probabilities. Since our algorithm learns a Bayesian model of the wireless link, it can be adapted to exploit prior knowledge often available in practical wireless networks. Through numerical results from simulated experiments, we demonstrate that Con-TS significantly outperforms state-of-the-art bandit algorithms proposed in the literature. Further, we compare Con-TS with the outer loop link adaptation (OLLA) scheme, which is the state-of-the-art in practical wireless networks and relies on carefully tuned offline link models. We show that Con-TS outperforms OLLA in simulations, further, it can elegantly incorporate information from the offline link models to substantially improve performance.

Includes members:
  Member "beyond the click through rate web link selection with multi level feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial sleeping bandits with fairness constraints")
  Member "efficient beam alignment in millimeter wave systems using contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")
  Member "unimodal thompson sampling for graph structured arms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient beam alignment in millimeter wave systems using contextual bandits")
  Member "dynamic rate and channel selection in cognitive radio systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient beam alignment in millimeter wave systems using contextual bandits")
  Member "bandits with knapsacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploration exploitation in constrained mdps")
  Member "optimal rate sampling in 802 11 systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "thresholding graph bandits with grapl")
  Member "further optimal regret bounds for thompson sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "delay adaptive learning in generalized linear contextual bandits")
  Member "coding versus arq in fading channels how reliable should the phy be":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "short packet transmission via variable length codes in the presence of noisy stop feedback")

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)