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 "collaborative filtering bandits"):
  Abstract: Classical collaborative filtering, and content-based filtering methods try to learn a static recommendation model given training data. These approaches are far from ideal in highly dynamic recommendation domains such as news recommendation and computational advertisement, where the set of items and users is very fluid. In this work, we investigate an adaptive clustering technique for content recommendation based on exploration-exploitation strategies in contextual multi-armed bandit settings. Our algorithm takes into account the collaborative effects that arise due to the interaction of the users with the items, by dynamically grouping users based on the items under consideration and, at the same time, grouping items based on the similarity of the clusterings induced over the users. The resulting algorithm thus takes advantage of preference patterns in the data in a way akin to collaborative filtering methods. We provide an empirical analysis on medium-size real-world datasets, showing scalability and increased prediction performance (as measured by click-through rate) over state-of-the-art methods for clustering bandits. We also provide a regret analysis within a standard linear stochastic noise setting.

Includes members:
  Member "distributed clustering of linear bandits in peer to peer networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "cascading bandits learning to rank in the cascade model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "combinatorial semi bandit in the non stationary environment")
  Member "online clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "a latent source model for online collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boolean kernels for collaborative filtering in top n item recommendation")
  Member "a gang of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data")
  Member "sample complexity of multi task reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "invariant causal prediction for block mdps")
  Member "hierarchical exploration for accelerating contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "conversational contextual bandit algorithm and application")
  Member "sample efficient nonstationary policy evaluation for contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "counterfactual reasoning and learning systems")

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)