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 "parallel contextual bandits in wireless handover optimization"):
  Abstract: As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual bandit framework arises as a natural candidate for such a task, its extension to a parallel setting is not straightforward: one needs to carefully adapt existing methods to fully leverage the multi-agent structure of this problem. We propose two approaches: one derived from a deterministic UCB-like method and the other relying on Thompson sampling. Thanks to its bayesian nature, the latter is intuited to better preserve the exploration-exploitation balance in the bandit batch. This is verified on toy experiments, where Thompson sampling shows robustness to the variability of the contexts. Finally, we apply both methods on a real base station network dataset and evidence that Thompson sampling outperforms both manual tuning and contextual UCB.

Includes members:
  Member "stochastic bandit models for delayed conversions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a feedback shift correction in predicting conversion rates under delayed feedback")
  Member "low rank bandits with latent mixtures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning near optimal policies with low inherent bellman error")
  Member "parallel predictive entropy search for batch global optimization of expensive objective functions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "one shot bayes opt with probabilistic population based training")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "online learning under delayed feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "logarithmic regret for adversarial online control")
  Member "parallel gaussian process optimization with upper confidence bound and pure exploration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "one shot bayes opt with probabilistic population based training")
  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 "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neural contextual bandits with ucb based exploration")

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.NI(Networking and Internet Architecture)