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 "faster kernel ridge regression using sketching and preconditioning"):
  Abstract: Kernel Ridge Regression (KRR) is a simple yet powerful technique for non-parametric regression whose computation amounts to solving a linear system. This system is usually dense and highly ill-conditioned. In addition, the dimensions of the matrix are the same as the number of data points, so direct methods are unrealistic for large-scale datasets. In this paper, we propose a preconditioning technique for accelerating the solution of the aforementioned linear system. The preconditioner is based on random feature maps, such as random Fourier features, which have recently emerged as a powerful technique for speeding up and scaling the training of kernel-based methods, such as kernel ridge regression, by resorting to approximations. However, random feature maps only provide crude approximations to the kernel function, so delivering state-of-the-art results by directly solving the approximated system requires the number of random features to be very large. We show that random feature maps can be much more effective in forming preconditioners, since under certain conditions a not-too-large number of random features is sufficient to yield an effective preconditioner. We empirically evaluate our method and show it is highly effective for datasets of up to one million training examples.

Includes members:
  Member "sharper bounds for regression and low rank approximation with regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably robust blackbox optimization for reinforcement learning")
  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 "hierarchically compositional kernels for scalable nonparametric learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm")
  Member "optimal approximate matrix product in terms of stable rank":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "implementing randomized matrix algorithms in parallel and distributed environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning")
  Member "sketching as a tool for numerical linear algebra":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "scalable kernel methods via doubly stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural kernels without tangents")
  Member "preconditioned krylov solvers for kernel regression":
    also connected through other hyperedges:
      (none)

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.NA(Numerical Analysis)