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 "a general scoring rule for randomized kernel approximation with application to canonical correlation analysis"):
  Abstract: Random features has been widely used for kernel approximation in large-scale machine learning. A number of recent studies have explored data-dependent sampling of features, modifying the stochastic oracle from which random features are sampled. While proposed techniques in this realm improve the approximation, their application is limited to a specific learning task. In this paper, we propose a general scoring rule for sampling random features, which can be employed for various applications with some adjustments. We first observe that our method can recover a number of data-dependent sampling methods (e.g., leverage scores and energy-based sampling). Then, we restrict our attention to a ubiquitous problem in statistics and machine learning, namely Canonical Correlation Analysis (CCA). We provide a principled guide for finding the distribution maximizing the canonical correlations, resulting in a novel data-dependent method for sampling features. Numerical experiments verify that our algorithm consistently outperforms other sampling techniques in the CCA task.

Includes members:
  Member "on sampling random features from empirical leverage scores implementation and theoretical guarantees":
    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 "random fourier features for kernel ridge regression approximation bounds and statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "on sampling random features from empirical leverage scores implementation and theoretical guarantees")
  Member "stochastic approximation for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems")
  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 "efficient globally convergent stochastic optimization for canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural network assisted nonlinear multiview component analysis identifiability and algorithm")
  Member "on the equivalence between kernel quadrature rules and random feature expansions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convolutional spectral kernel learning")

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)