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: "faster subset selection for matrices and applications"
  Abstract: We study subset selection for matrices defined as follows: given a matrix $\matX \in \R^{n \times m}$ ($m > n$) and an oversampling parameter $k$ ($n \le k \le m$), select a subset of $k$ columns from $\matX$ such that the pseudo-inverse of the subsampled matrix has as smallest norm as possible. In this work, we focus on the Frobenius and the spectral matrix norms. We describe several novel (deterministic and randomized) approximation algorithms for this problem with approximation bounds that are optimal up to constant factors. Additionally, we show that the combinatorial problem of finding a low-stretch spanning tree in an undirected graph corresponds to subset selection, and discuss various implications of this reduction.

Connected through hyperedge HE-1 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "two models of double descent for weak features"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-2 (induced by source paper "recommendation on a budget column space recovery from partially observed entries with random or active sampling"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "streaming principal component analysis from incomplete data"
    - "matrix completion has no spurious local minimum"
    - "subspace learning with partial information"
    - "understanding alternating minimization for matrix completion"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"

Connected through hyperedge HE-3 (induced by source paper "bayesian experimental design using regularized determinantal point processes"):
  with members:
    - "distributed estimation of the inverse hessian by determinantal averaging"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"

Connected through hyperedge HE-4 (induced by source paper "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"):
  with members:
    - "correcting the bias in least squares regression with volume rescaled sampling"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "reverse iterative volume sampling for linear regression"
    - "leveraged volume sampling for linear regression"
    - "lectures on randomized numerical linear algebra"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"

Connected through hyperedge HE-5 (induced by source paper "subset selection for matrices with fixed blocks"):
  with members:
    - "reverse iterative volume sampling for linear regression"
    - "discrete signal processing on graphs sampling theory"
    - "powers of tensors and fast matrix multiplication"
    - "deterministic feature selection for k means clustering"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-6 (induced by source paper "reverse iterative volume sampling for linear regression"):
  with members:
    - "active regression via linear sample sparsification"
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "near optimal coresets for least squares regression"
    - "low rank approximation and regression in input sparsity time"
    - "fast approximation of matrix coherence and statistical leverage"
    - "optimal column based low rank matrix reconstruction"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-7 (induced by source paper "eliciting worker preference for task completion"):
  with members:
    - "feature based task recommendation in crowdsourcing with implicit observations"
    - "the labor economics of paid crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "sampling and multilevel coarsening algorithms for fast matrix approximations"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "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"
    - "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.DS(Data Structures and Algorithms)