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 "recursive sampling for the nystr om method"):
  Abstract: We give the first algorithm for kernel Nystr\"om approximation that runs in *linear time in the number of training points* and is provably accurate for all kernel matrices, without dependence on regularity or incoherence conditions. The algorithm projects the kernel onto a set of $s$ landmark points sampled by their *ridge leverage scores*, requiring just $O(ns)$ kernel evaluations and $O(ns^2)$ additional runtime. While leverage score sampling has long been known to give strong theoretical guarantees for Nystr\"om approximation, by employing a fast recursive sampling scheme, our algorithm is the first to make the approach scalable. Empirically we show that it finds more accurate, lower rank kernel approximations in less time than popular techniques such as uniformly sampled Nystr\"om approximation and the random Fourier features method.

Includes members:
  Member "relative error embeddings for the gaussian kernel distance":
    also connected through other hyperedges:
      (none)
  Member "fast dpp sampling for nystr om with application to kernel methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "submodular batch selection for training deep neural networks")
  Member "on column selection in approximate kernel canonical correlation analysis":
    also connected through other hyperedges:
      (none)
  Member "large scale kernel learning using block coordinate descent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance")
  Member "ridge leverage scores for low rank approximation":
    also connected through other hyperedges:
      (none)
  Member "input sparsity time low rank approximation via ridge leverage score sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation")
  Member "streaming kernel principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the gaussiansketch for almost relative error kernel distance")
  Member "frequent directions simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deterministic streaming sketch for ridge regression")

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)