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: "improved bounds on sample size for implicit matrix trace estimators"
  Abstract: This article is concerned with Monte Carlo methods for the estimation of the trace of an implicitly given matrix $$A$$A whose information is only available through matrix-vector products. Such a method approximates the trace by an average of $$N$$N expressions of the form $$ \mathbf{w} ^t (A \mathbf{w} )$$wt(Aw), with random vectors $$ \mathbf{w} $$w drawn from an appropriate distribution. We prove, discuss and experiment with bounds on the number of realizations $$N$$N required to guarantee a probabilistic bound on the relative error of the trace estimation upon employing Rademacher (Hutchinson), Gaussian and uniform unit vector (with and without replacement) probability distributions. In total, one necessary bound and six sufficient bounds are proved, improving upon and extending similar estimates obtained in the seminal work of Avron and Toledo (JACM 58(2). Article 8, 2011) in several dimensions. We first improve their bound on $$N$$N for the Hutchinson method, dropping a term that relates to $$\mathrm{rank}(A)$$rank(A) and making the bound comparable with that for the Gaussian estimator. We further prove new sufficient bounds for the Hutchinson, Gaussian and unit vector estimators, as well as a necessary bound for the Gaussian estimator, which depend more specifically on properties of matrix $$A$$A. As such, they may suggest the type of matrix for which one distribution or another provides a particularly effective or relatively ineffective stochastic estimation method.

Connected through hyperedge HE-1 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-2 (induced by source paper "a randomized algorithm for preconditioner selection"):
  with members:
    - "faster kernel ridge regression using sketching and preconditioning"
    - "sketching as a tool for numerical linear algebra"
    - "optimal query complexity for estimating the trace of a matrix"
    - "spectral condition number estimation of large sparse matrices"
    - "compressed matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "randomized linear algebra approaches to estimate the von neumann entropy of density matrices"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "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"
    - "sketching and streaming entropy via approximation theory"
    - "faster least squares approximation"

Connected through hyperedge HE-4 (induced by source paper "vbald variational bayesian approximation of log determinants"):
  with members:
    - "entropic trace estimates for log determinants"
    - "faster greedy map inference for determinantal point processes"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"

Connected through hyperedge HE-5 (induced by source paper "approximations of schatten norms via taylor expansions"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"

Connected through hyperedge HE-6 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "fast estimation of approximate matrix ranks using spectral densities"):
  with members:
    - "relations among some low rank subspace recovery models"
    - "efficient estimation of eigenvalue counts in an interval"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "a data scalable randomized misfit approach for solving large scale pde constrained inverse problems"):
  with members:
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "data completion and stochastic algorithms for pde inversion problems with many measurements"
    - "robust inversion dimensionality reduction and randomized sampling"

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)