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 "laplacian regularized graph bandits algorithms and theoretical analysis"):
  Abstract: We study contextual multi-armed bandit problems in the case of multiple users, where we exploit the structure in the user domain to reduce the cumulative regret. Specifically, we model user relation as a graph, and assume that the parameters (preferences) of users form smooth signals on the graph. This leads to a graph Laplacian-regularized estimator, for which we propose a novel bandit algorithm whose performance depends on a notion of local smoothness on the graph. We provide a closed-form solution to the estimator, enabling a theoretical analysis on the convergence property of the estimator as well as single-user upper confidence bound (UCB) and cumulative regret of the proposed bandit algorithm. Furthermore, we show that the regret scales linearly with the local smoothness measure, which approaches zero for densely connected graph. The single-user UCB also allows us to further propose an extension of the bandit algorithm, whose computational complexity scales linearly with the number of users. We support theoretical claims with empirical evidences, and demonstrate the advantage of the proposed algorithm in comparison with state-of-the-art graph-based bandit algorithms on both synthetic and real-world datasets.

Includes members:
  Member "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "algorithmic fairness")
  Member "alternating linear bandits for online matrix factorization recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "error analysis on graph laplacian regularized estimator")
  Member "almost optimal algorithms for linear stochastic bandits with heavy tailed payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "better algorithms for stochastic bandits with adversarial corruptions")
  Member "graph based recommendation system":
    also connected through other hyperedges:
      (none)
  Member "horde of bandits using gaussian markov random fields":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear bandits with low rank structure")
  Member "distributed clustering of linear bandits in peer to peer networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret bounds for decentralized learning in cooperative multi agent dynamical systems")
  Member "collaborative filtering 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 "learning laplacian matrix in smooth graph signal representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph signal processing part iii machine learning on graphs from graph topology to applications")

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)