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 "random fourier features via fast surrogate leverage weighted sampling"):
  Abstract: In this paper, we propose a fast surrogate leverage weighted sampling strategy to generate refined random Fourier features for kernel approximation. Compared to the current state-of-the-art method that uses the leverage weighted scheme [Li-ICML2019], our new strategy is simpler and more effective. It uses kernel alignment to guide the sampling process and it can avoid the matrix inversion operator when we compute the leverage function. Given n observations and s random features, our strategy can reduce the time complexity from O(ns^2+s^3) to O(ns^2), while achieving comparable (or even slightly better) prediction performance when applied to kernel ridge regression (KRR). In addition, we provide theoretical guarantees on the generalization performance of our approach, and in particular characterize the number of random features required to achieve statistical guarantees in KRR. Experiments on several benchmark datasets demonstrate that our algorithm achieves comparable prediction performance and takes less time cost when compared to [Li-ICML2019].

Includes members:
  Member "nystrom landmark sampling and regularized christoffel functions":
    also connected through other hyperedges:
      (none)
  Member "but how does it work in theory linear svm with random features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning vector valued functions with local rademacher complexity")
  Member "low precision random fourier features for memory constrained kernel approximation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the downstream performance of compressed word embeddings")
  Member "quadrature based features for kernel approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalization guarantees for sparse kernel approximation with entropic optimal features")
  Member "random fourier features for kernel ridge regression approximation bounds and statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "gaussian quadrature for kernel features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast estimation of information theoretic learning descriptors using explicit inner product spaces")
  Member "orthogonal random features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "input sparsity time low rank approximation via ridge leverage score sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation")

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)