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: "fast estimation of approximate matrix ranks using spectral densities"
  Abstract: Many machine learning and data-related applications require the knowledge of approximate ranks of large data matrices at hand. This letter presents two computationally inexpensive techniques to estimate the approximate ranks of such matrices. These techniques exploit approximate spectral densities, popular in physics, which are probability density distributions that measure the likelihood of finding eigenvalues of the matrix at a given point on the real line. Integrating the spectral density over an interval gives the eigenvalue count of the matrix in that interval. Therefore, the rank can be approximated by integrating the spectral density over a carefully selected interval. Two different approaches are discussed to estimate the approximate rank, one based on Chebyshev polynomials and the other based on the Lanczos algorithm. In order to obtain the appropriate interval, it is necessary to locate a gap between the eigenvalues that correspond to noise and the relevant eigenvalues that contribute to the mat...

Connected through hyperedge HE-1 (induced by source paper "find the dimension that counts fast dimension estimation and krylov pca"):
  with members:
    - "low rank approximation and decomposition of large matrices using error correcting codes"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "spectrum adapted polynomial approximation for matrix functions"):
  with members:
    - "compressive spectral clustering"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-3 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"

Connected through hyperedge HE-4 (induced by source paper "low rank approximation and decomposition of large matrices using error correcting codes"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "restricted isometry property of random subdictionaries"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "sketching as a tool for numerical linear algebra"
    - "low rank matrix approximation in linear time"
    - "low rank approximation and regression in input sparsity time"
    - "on almost disjunct matrices for group testing"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"

Connected through hyperedge HE-5 (induced by source paper "sampling and multilevel coarsening algorithms for fast matrix approximations"):
  with members:
    - "low rank approximation and decomposition of large matrices using error correcting codes"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "simple and deterministic matrix sketching"
    - "faster subset selection for matrices and applications"
    - "randomized algorithms for matrices and data"
    - "spectral sparsification of graphs"
    - "an improved approximation algorithm for the column subset selection problem"

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.NA(Numerical Analysis)