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 spsd matrix approximation and cur matrix decomposition"
  Abstract: Symmetric positive semi-definite (SPSD) matrix approximation methods have been extensively used to speed up large-scale eigenvalue computation and kernel learning methods. The standard sketch based method, which we call the prototype model, produces relatively accurate approximations, but is inefficient on large square matrices. The Nystr\"om method is highly efficient, but can only achieve low accuracy. In this paper we propose a novel model that we call the {\it fast SPSD matrix approximation model}. The fast model is nearly as efficient as the Nystr\"om method and as accurate as the prototype model. We show that the fast model can potentially solve eigenvalue problems and kernel learning problems in linear time with respect to the matrix size $n$ to achieve $1+\epsilon$ relative-error, whereas both the prototype model and the Nystr\"om method cost at least quadratic time to attain comparable error bound. Empirical comparisons among the prototype model, the Nystr\"om method, and our fast model demonstrate the superiority of the fast model. We also contribute new understandings of the Nystr\"om method. The Nystr\"om method is a special instance of our fast model and is approximation to the prototype model. Our technique can be straightforwardly applied to make the CUR matrix decomposition more efficiently computed without much affecting the accuracy.

Connected through hyperedge HE-1 (induced by source paper "fast generalized matrix regression with applications in machine learning"):
  with members:
    - "practical sketching algorithms for low rank matrix approximation"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-2 (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"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "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-3 (induced by source paper "scalable kernel k means clustering with nystrom approximation relative error bounds"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "mllib machine learning in apache spark"
    - "the singular value decomposition applications and beyond"
    - "the hardness of approximation of euclidean k means"
    - "sketching as a tool for numerical linear algebra"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"

Connected through hyperedge HE-4 (induced by source paper "tighter bound of sketched generalized matrix approximation"):
  with members:
    - "towards more efficient symmetric matrix sketching and the 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-5 (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"
    - "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"
    - "the modified nystrom method theories algorithms and extension"
    - "scalable kernel methods via doubly stochastic gradients"

Connected through hyperedge HE-6 (induced by source paper "a simple approach to optimal cur decomposition"):
  with members:
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "spsd matrix approximation vis column selection theories algorithms and extensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-7 (induced by source paper "spsd matrix approximation vis column selection theories algorithms and extensions"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "matrix coherence and the nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "fast approximation of matrix coherence and statistical leverage"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"

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)