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: "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
  Abstract: We introduce a novel algorithm for approximating the logarithm of the determinant of a symmetric positive definite (SPD) matrix. The algorithm is randomized and approximates the traces of a small number of matrix powers of a specially constructed matrix, using the method of Avron and Toledo~\cite{AT11}. From a theoretical perspective, we present additive and relative error bounds for our algorithm. Our additive error bound works for any SPD matrix, whereas our relative error bound works for SPD matrices whose eigenvalues lie in the interval $(\theta_1,1)$, with $0<\theta_1<1$; the latter setting was proposed in~\cite{icml2015_hana15}. From an empirical perspective, we demonstrate that a C++ implementation of our algorithm can approximate the logarithm of the determinant of large matrices very accurately in a matter of seconds.

Connected through hyperedge HE-1 (induced by source paper "schatten norms in matrix streams hello sparsity goodbye dimension"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "approximating the spectrum of a graph"
    - "embeddings of schatten norms with applications to data streams"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "spectrum estimation from samples"
    - "on approximating functions of the singular values in a stream"
    - "matrix norms in data streams faster multi pass and row order"

Connected through hyperedge HE-2 (induced by source paper "invertible residual networks"):
  with members:
    - "hybrid models with deep and invertible features"
    - "image transformer"
    - "stochastic chebyshev gradient descent for spectral optimization"
    - "backpropagation for implicit spectral densities"
    - "sorting out lipschitz function approximation"
    - "analyzing inverse problems with invertible neural networks"
    - "the singular values of convolutional layers"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"

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"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "improved bounds on sample size for implicit matrix trace estimators"
    - "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"
    - "improved bounds on sample size for implicit matrix trace estimators"

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"
    - "improved bounds on sample size for implicit matrix trace estimators"

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 "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"):
  with members:
    - "density independent algorithms for sparsifying k step random walks"
    - "sampling random spanning trees faster than matrix multiplication"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "computing the log determinant of symmetric diagonally dominant matrices in near linear time"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-8 (induced by source paper "faster greedy map inference for determinantal point processes"):
  with members:
    - "on sampling and greedy map inference of constrained determinantal point processes"
    - "block wise map inference for determinantal point processes with application to change point detection"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "efficient sampling for k determinantal point processes"
    - "lazier than lazy greedy"
    - "learning determinantal point processes"

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.DS(Data Structures and Algorithms)