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 "latent contextual bandits and their application to personalized recommendations for new users"):
  Abstract: Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.

Includes members:
  Member "a survey on contextual multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive metamorphic testing with contextual bandits")
  Member "online clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "taming the monster a fast and simple algorithm for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "generalized thompson sampling for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the prior sensitivity of thompson sampling")
  Member "spectral experts for estimating mixtures of linear regressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "meta learning for mixed linear regression")
  Member "hierarchical exploration for accelerating contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conversational contextual bandit algorithm and application")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")
  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.LG(Machine Learning)