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: "matrix coherence and the nystrom method"
  Abstract: The Nystrom method is an efficient technique used to speed up large-scale learning applications by generating low-rank approximations. Crucial to the performance of this technique is the assumption that a matrix can be well approximated by working exclusively with a subset of its columns. In this work we relate this assumption to the concept of matrix coherence, connecting coherence to the performance of the Nystrom method. Making use of related work in the compressed sensing and the matrix completion literature, we derive novel coherence-based bounds for the Nystrom method in the low-rank setting. We then present empirical results that corroborate these theoretical bounds. Finally, we present more general empirical results for the full-rank setting that convincingly demonstrate the ability of matrix coherence to measure the degree to which information can be extracted from a subset of columns.

Connected through hyperedge HE-1 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "network topology identification from spectral templates"
    - "compressive spectral clustering"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"

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"
    - "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"
    - "the modified nystrom method theories algorithms and extension"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "optimal cur matrix decompositions"

Connected through hyperedge HE-3 (induced by source paper "streaming kernel principal component analysis"):
  with members:
    - "improved practical matrix sketching with guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "fastfood approximate kernel expansions in loglinear time"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "relative errors for deterministic low rank matrix approximations"
    - "low rank approximation and regression in input sparsity time"
    - "random feature maps for dot product kernels"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "far field compression for fast kernel summation methods in high dimensions"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "optimized m2l kernels for the chebyshev interpolation based fast multipole method"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-5 (induced by source paper "spsd matrix approximation vis column selection theories algorithms and extensions"):
  with members:
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "on data preconditioning for regularized loss minimization"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "sharp analysis of low rank kernel matrix approximations"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-7 (induced by source paper "efficient algorithms and error analysis for the modified nystrom method"):
  with members:
    - "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"
    - "near optimal column based matrix reconstruction"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-8 (induced by source paper "revisiting the nystrom method for improved large scale machine learning"):
  with members:
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "sharp analysis of low rank kernel matrix approximations"
    - "stochastic low rank kernel learning for regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "localization on low order eigenvectors of data matrices"
    - "lsrn a parallel iterative solver for strongly over or under determined systems"

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.AI(Artificial Intelligence)