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: "the modified nystrom method theories algorithms and extension"
  Abstract: Many kernel methods suffer from high time and space complexities, so they are prohibitive in big-data applications. To tackle the computational challenge, the Nystr\"om method has been extensively used to reduce time and space complexities by sacrificing some accuracy. The Nystr\"om method speedups computation by constructing an approximation of the kernel matrix in question using only a few columns of the matrix. Recently, a variant of the Nystr\"om method called the modified Nystr\"om method has demonstrated significant improvement over the standard Nystr\"om method in approximation accuracy, both theoretically and empirically. #R##N#In this paper we provide theoretical analysis, efficient algorithms, and a simple but highly accurate extension for the modified Nystr\"om method. First, we prove that the modified Nystr\"om method is exact under certain conditions, and we establish a lower error bound for the modified Nystr\"om method. Second, we develop two efficient algorithms to make the modified Nystr\"om method efficient and practical. We devise a simple column selection algorithm with a provable error bound. With the selected columns at hand, we propose an algorithm that computes the modified Nystr\"om approximation in lower time complexity than the approach in the previous work. Third, the extension which we call {\it the SS-Nystr\"om method} has much stronger error bound than the modified Nystr\"om method, especially when the spectrum of the kernel matrix decays slowly. Our proposed SS-Nystr\"om can be computed nearly as efficiently as the modified Nystr\"om method. Finally, experiments on real-world datasets demonstrate that the proposed column selection algorithm is both efficient and accurate and that the SS-Nystr\"om method always leads to much higher kernel approximation accuracy than the standard/modified Nystr\"om method.

Connected through hyperedge HE-1 (induced by source paper "the singular value decomposition applications and beyond"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient symmetric matrix sketching and the cur matrix decomposition"
    - "improved analyses of the randomized power method and block lanczos method"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-2 (induced by source paper "a practical guide to randomized matrix computations with matlab implementations"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"

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)