human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "serverless straggler mitigation using local error correcting codes"):
  with members:
    - "polar coded distributed matrix multiplication"
    - "oversketched newton fast convex optimization for serverless systems"
    - "cloud programming simplified a berkeley view on serverless computing"
    - "collaborative decoding of polynomial codes for distributed computation"
    - "serverless computing one step forward two steps back"
    - "learning a code machine learning for approximate non linear coded computation"
    - "speeding up distributed machine learning using codes"
    - "oversketch approximate matrix multiplication for the cloud"

Connected through hyperedge HE-2 (induced by source paper "oblivious sketching of high degree polynomial kernels"):
  with members:
    - "almost optimal tensor sketch"
    - "a universal sampling method for reconstructing signals with simple fourier transforms"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "recursive sampling for the nystr om method"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-3 (induced by source paper "a randomized algorithm for preconditioner selection"):
  with members:
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "sketching as a tool for numerical linear algebra"
    - "optimal query complexity for estimating the trace of a matrix"
    - "spectral condition number estimation of large sparse matrices"
    - "compressed matrix multiplication"

Connected through hyperedge HE-4 (induced by source paper "distributed learning with random features"):
  with members:
    - "orthogonal random features"
    - "distributed learning with regularized least squares"
    - "distributed mean estimation with limited communication"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-5 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "eigenvalue decay implies polynomial time learnability for neural networks"
    - "efficient second order online learning by sketching"
    - "solving ridge regression using sketched preconditioned svrg"
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-6 (induced by source paper "hashing based estimators for kernel density in high dimensions"):
  with members:
    - "coresets for kernel regression"
    - "provably useful kernel matrix approximation in linear time"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "recursive sampling for the nystr om method"
    - "sample optimal density estimation in nearly linear time"
    - "optimal data dependent hashing for approximate near neighbors"

Connected through hyperedge HE-7 (induced by source paper "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm"):
  with members:
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "large scale kernel learning using block coordinate descent"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "faster least squares approximation"

Connected through hyperedge HE-8 (induced by source paper "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"):
  with members:
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "sketching as a tool for numerical linear algebra"
    - "sharp analysis of low rank kernel matrix approximations"

Question: Please tell me which class the center node 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)