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 "on sampling random features from empirical leverage scores implementation and theoretical guarantees"):
  Abstract: Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an optimized distribution for features based on an infinite-dimensional integral operator (depending on input distribution), which is impractical to sample from. Focusing on empirical leverage scores in this paper, we establish an out-of-sample performance bound, revealing an interesting trade-off between the approximated kernel and the eigenvalue decay of another kernel in the domain of random features defined based on data distribution. Our experiments verify that the empirical algorithm consistently outperforms vanilla Monte Carlo sampling, and with a minor modification the method is even competitive to supervised data-dependent kernel learning, without using the output (label) information.

Includes members:
  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 "learning bounds for greedy approximation with explicit feature maps from multiple kernels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "data driven random fourier features using stein effect":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis")
  Member "orthogonal random features":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "on the equivalence between kernel quadrature rules and random feature expansions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convolutional spectral kernel learning")
  Member "fastfood approximate kernel expansions in loglinear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "random feature maps for dot product kernels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "harmonizing transferability and discriminability for adapting object detectors")

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)