human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "topics in matrix sampling algorithms"):
  Abstract: We study three fundamental problems of Linear Algebra, lying in the heart of various Machine Learning applications, namely: (i) Low-rank Column-based Matrix Approximation, (ii) Coreset Construction in Least-Squares Regression, and (iii) Feature Selection in k-means Clustering. A high level description of these problems is as follows: given a matrix A and an integer r, what are the r most “important” columns (or rows) in A? A more detailed description is given momentarily. #R##N#1. Low-rank Column-based Matrix Approximation. We are given a matrix A and a target rank k. The goal is to select a subset of columns of A and, by using only these columns, compute a rank k approximation to A that is as good as the rank k approximation that would have been obtained by using all the columns.#R##N#2. Coreset Construction in Least-Squares Regression. We are given a matrix A and a vector b. Consider the (over-constrained) least-squares problem of minimizing ||Ax − b||2, over all vectors x ∈ D . The domain D represents the constraints on the solution and can be arbitrary. The goal is to select a subset of the rows of A and b and, by using only these rows, find a solution vector that is as good as the solution vector that would have been obtained by using all the rows.#R##N#3. Feature Selection in K-means Clustering. We are given a set of points described with respect to a large number of features. The goal is to select a subset of the features and, by using only this subset, obtain a k-partition of the points that is as good as the partition that would have been obtained by using all the features.#R##N#We present novel algorithms for all three problems mentioned above. Our results can be viewed as follow-up research to a line of work known as “Matrix Sampling Algorithms”. Frieze et al. [59] presented the first such algorithm for the Low-rank Matrix Approximation problem. Since then, such algorithms have been developed for several other problems, e.g. Regression [47], Graph Sparsification [131], and Linear Equation Solving [128]. Our contributions to this line of research are: (i) improved algorithms for Low-rank Matrix Approximation and Regression (ii) algorithms for a new problem domain ( K-means Clustering).

Includes members:
  Member "a matrix hyperbolic cosine algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "optimal column based low rank matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "near optimal column based matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "a unified framework for approximating and clustering data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "lasserre hierarchy higher eigenvalues and approximation schemes for quadratic integer programming with psd objectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximation limits of linear programs beyond hierarchies")
  Member "algorithmic and statistical perspectives on large scale data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting the nystrom method for improved large scale machine learning")
  Member "random projections for k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improved subsampled randomized hadamard transform for linear svm")

Question: Please tell me which class the source paper 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)