human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "unimodal thompson sampling for graph structured arms"
  Abstract: We study, to the best of our knowledge, the first Bayesian algorithm for unimodal Multi-Armed Bandit (MAB) problems with graph structure. In this setting, each arm corresponds to a node of a graph and each edge provides a relationship, unknown to the learner, between two nodes in terms of expected reward. Furthermore, for any node of the graph there is a path leading to the unique node providing the maximum expected reward, along which the expected reward is monotonically increasing. Previous results on this setting describe the behavior of frequentist MAB algorithms. In our paper, we design a Thompson Sampling-based algorithm whose asymptotic pseudo-regret matches the lower bound for the considered setting. We show that -as it happens in a wide number of scenarios- Bayesian MAB algorithms dramatically outperform frequentist ones. In particular, we provide a thorough experimental evaluation of the performance of our and state-of-the-art algorithms as the properties of the graph vary.

Connected through hyperedge HE-1 (induced by source paper "constrained thompson sampling for wireless link optimization"):
  with members:
    - "beyond the click through rate web link selection with multi level feedback"
    - "efficient beam alignment in millimeter wave systems using contextual bandits"
    - "dynamic rate and channel selection in cognitive radio systems"
    - "bandits with knapsacks"
    - "optimal rate sampling in 802 11 systems"
    - "further optimal regret bounds for thompson sampling"
    - "coding versus arq in fading channels how reliable should the phy be"
    - "multi armed bandits in metric spaces"

Connected through hyperedge HE-2 (induced by source paper "efficient beam alignment in millimeter wave systems using contextual bandits"):
  with members:
    - "agile millimeter wave networks with provable guarantees"
    - "millimeter wave beam selection using out of band spatial information"
    - "energy efficient power and bandwidth allocation in an integrated sub 6 ghz millimeter wave system"
    - "coordinated initial access in millimetre wave standalone networks"
    - "unimodal bandits regret lower bounds and optimal algorithms"
    - "directional cell discovery in millimeter wave cellular networks"
    - "dynamic rate and channel selection in cognitive radio systems"
    - "optimal rate sampling in 802 11 systems"

Question: Please tell me which class the center node 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)