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 "toward building conversational recommender systems a contextual bandit approach"):
  Abstract: Contextual bandit algorithms have gained increasing popularity in recommender systems, because they can learn to adapt recommendations by making exploration-exploitation trade-off. Recommender systems equipped with traditional contextual bandit algorithms are usually trained with behavioral feedback (e.g., clicks) from users on items. The learning speed can be slow because behavioral feedback by nature does not carry sufficient information. As a result, extensive exploration has to be performed. To address the problem, we propose conversational recommendation in which the system occasionally asks questions to the user about her interest. We first generalize contextual bandit to leverage not only behavioral feedback (arm-level feedback), but also verbal feedback (users' interest on categories, topics, etc.). We then propose a new UCB- based algorithm, and theoretically prove that the new algorithm can indeed reduce the amount of exploration in learning. We also design several strategies for asking questions to further optimize the speed of learning. Experiments on synthetic data, Yelp data, and news recommendation data from Toutiao demonstrate the efficacy of the proposed algorithm.

Includes members:
  Member "conversational recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "active learning in recommendation systems with multi level user preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conversational contextual bandit algorithm and application")
  Member "a gang of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data")
  Member "hierarchical exploration for accelerating contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conversational contextual bandit algorithm and application")
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "unbiased offline evaluation of contextual bandit based news article recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised contextual bandits in computer vision")
  Member "multi armed bandits in metric spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)