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: "large scale log determinant computation through stochastic chebyshev expansions"
  Abstract: Logarithms of determinants of large positive definite matrices appear ubiquitously in machine learning applications including Gaussian graphical and Gaussian process models, partition functions of discrete graphical models, minimum-volume ellipsoids, metric learning and kernel learning. Log-determinant computation involves the Cholesky decomposition at the cost cubic in the number of variables, i.e., the matrix dimension, which makes it prohibitive for large-scale applications. We propose a linear-time randomized algorithm to approximate log-determinants for very large-scale positive definite and general non-singular matrices using a stochastic trace approximation, called the Hutchinson method, coupled with Chebyshev polynomial expansions that both rely on efficient matrix-vector multiplications. We establish rigorous additive and multiplicative approximation error bounds depending on the condition number of the input matrix. In our experiments, the proposed algorithm can provide very high accuracy solutions at orders of magnitude faster time than the Cholesky decomposition and Schur completion, and enables us to compute log-determinants of matrices involving tens of millions of variables.

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 "exponential family estimation via adversarial dynamics embedding"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "hamiltonian variational auto encoder"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"

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"
    - "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 "scaling gaussian process regression with derivatives"):
  with members:
    - "product kernel interpolation for scalable gaussian processes"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "the human kernel"
    - "gaussian processes for big data"

Connected through hyperedge HE-5 (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"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "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-6 (induced by source paper "stability of the lanczos method for matrix function approximation"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "principal component projection without principal component analysis"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "the noisy power method a meta algorithm with applications"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "a note on preconditioning by low stretch spanning trees"

Connected through hyperedge HE-7 (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"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "block wise map inference for determinantal point processes with application to change point detection"
    - "efficient sampling for k determinantal point processes"
    - "lazier than lazy greedy"
    - "learning determinantal point processes"

Connected through hyperedge HE-8 (induced by source paper "entropic trace estimates for log determinants"):
  with members:
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"

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)