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 "learning contextual bandits in a non stationary environment"):
  Abstract: Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.

Includes members:
  Member "a change detection based framework for piecewise stationary multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "efficient contextual bandits in non stationary worlds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted linear bandits for non stationary environments")
  Member "misspecified linear bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning near optimal policies with low inherent bellman error")
  Member "on context dependent clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "augmenting visual question answering with semantic frame information in a multitask learning approach")
  Member "collaborative filtering bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "online clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "a gang of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data")
  Member "unbiased offline evaluation of contextual bandit based news article recommendation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "self supervised contextual bandits in computer vision")

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)