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: "low rank matrix approximation in linear time"
  Abstract: $\newcommand{\MatA}{\mathcal{M}}$ $\newcommand{\eps}{\varepsilon}$ $\newcommand{\NSize}{\mathsf{N}{}}$ $\newcommand{\MatB}{\mathcal{B}}$ $\newcommand{\Fnorm}[1]{\left\| {#1} \right\|_F}$ $\newcommand{\PrcOpt}[2]{\mu_{\mathrm{opt}}\pth{#1, #2}}$ $\newcommand{\pth}[1]{\left(#1\right)}$ #R##N#Given a matrix $\MatA$ with $n$ rows and $d$ columns, and fixed $k$ and $\eps$, we present an algorithm that in linear time (i.e., $O(\NSize )$) computes a $k$-rank matrix $\MatB$ with approximation error $\Fnorm{\MatA - \MatB}^2 \leq (1+\eps) \PrcOpt{\MatA}{k}$, where $\NSize = n d$ is the input size, and $\PrcOpt{\MatA}{k}$ is the minimum error of a $k$-rank approximation to $\MatA$. #R##N#This algorithm succeeds with constant probability, and to our knowledge it is the first linear-time algorithm to achieve multiplicative approximation.

Connected through hyperedge HE-1 (induced by source paper "multiplicative rank 1 approximation using length squared sampling"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "faster least squares approximation"

Connected through hyperedge HE-2 (induced by source paper "low rank approximation and decomposition of large matrices using error correcting codes"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "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 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-3 (induced by source paper "tighter low rank approximation via sampling the leveraged element"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fast matrix completion without the condition number"
    - "relative errors for deterministic low rank matrix approximations"
    - "principal component analysis and higher correlations for distributed data"
    - "understanding alternating minimization for matrix completion"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "simple and deterministic matrix sketching"

Connected through hyperedge HE-4 (induced by source paper "matrix compression using the nystro om method"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"

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.CG(Computational Geometry)