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: "scalable kernel k means clustering with nystrom approximation relative error bounds"
  Abstract: Kernel $k$-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear $k$-means clustering algorithm. However, kernel $k$-means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel approximation, e.g., the Nystrom method, has been applied in previous works to approximately solve kernel learning problems when both of the above conditions are present. This work analyzes the application of this paradigm to kernel $k$-means clustering, and shows that applying the linear $k$-means clustering algorithm to $\frac{k}{\epsilon} (1 + o(1))$ features constructed using a so-called rank-restricted Nystrom approximation results in cluster assignments that satisfy a $1 + \epsilon$ approximation ratio in terms of the kernel $k$-means cost function, relative to the guarantee provided by the same algorithm without the use of the Nystrom method. As part of the analysis, this work establishes a novel $1 + \epsilon$ relative-error trace norm guarantee for low-rank approximation using the rank-restricted Nystrom approximation. Empirical evaluations on the $8.1$ million instance MNIST8M dataset demonstrate the scalability and usefulness of kernel $k$-means clustering with Nystrom approximation. This work argues that spectral clustering using Nystrom approximation---a popular and computationally efficient, but theoretically unsound approach to non-linear clustering---should be replaced with the efficient and theoretically sound combination of kernel $k$-means clustering with Nystrom approximation. The superior performance of the latter approach is empirically verified.

Connected through hyperedge HE-1 (induced by source paper "diversity sampling is an implicit regularization for kernel methods"):
  with members:
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "does learning require memorization a short tale about a long tail"
    - "sharp analysis of low rank kernel matrix approximations"

Connected through hyperedge HE-2 (induced by source paper "fast kernel k means clustering using incomplete cholesky factorization"):
  with members:
    - "sparse algorithm for robust lssvm in primal space"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "nearly optimal risk bounds for kernel k means"):
  with members:
    - "on the equivalence between quadrature rules and random features"
    - "learning manifolds with k means and k flats"

Connected through hyperedge HE-4 (induced by source paper "improving classification performance by feature space transformations and model selection"):
  with members:
    - "lectures on randomized numerical linear algebra"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-5 (induced by source paper "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm"):
  with members:
    - "large scale kernel learning using block coordinate descent"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "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"

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.LG(Machine Learning)