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: "towards more efficient symmetric matrix sketching and the cur matrix decomposition"
  Abstract: Matrix sketching schemes and the Nystr\"om method have both been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. Matrix sketching methods produce accurate matrix approximations, but they are only computationally efficient on skinny matrices where one of the matrix dimensions is relatively small. In particular, they are not efficient on large square matrices. The Nystr\"om method, on the other hand, is highly efficient on symmetric (and thus square) matrices, but can only achieve low matrix approximation accuracy. In this paper we propose a novel combination of the sketching method and the Nystr\"om method to improve their efficiency/effectiveness, leading to a novel approximation which we call the Sketch-Nystr\"om method. The Sketch-Nystr\"om method is computationally nearly as efficient as the Nystr\"om method on symmetric matrices with approximation accuracy comparable to that of the sketching method. We show theoretically that the Sketch-Nystr\"om method can potentially solve eigenvalue problems and kernel learning problems in linear time with respect to the matrix size to achieve $1+\epsilon$ relative-error, whereas the sketch methods and the Nystr\"om method cost at least quadratic time to attain comparable error bound. Our technique can be straightforwardly applied to make the CUR matrix decomposition more efficiently computed without much affecting the accuracy. Empirical experiments demonstrate the effectiveness of the proposed methods.

Connected through hyperedge HE-1 (induced by source paper "tighter bound of sketched generalized matrix approximation"):
  with members:
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "improved distributed principal component analysis"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the modified nystrom method theories algorithms and extension"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank 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)