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: "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).

Connected through hyperedge HE-1 (induced by source paper "near optimal coresets for least squares regression"):
  with members:
    - "principal component analysis and higher correlations for distributed data"
    - "near optimal column based matrix reconstruction"
    - "twice ramanujan sparsifiers"
    - "random projections for the nonnegative least squares problem"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-2 (induced by source paper "improved matrix algorithms via the subsampled randomized hadamard transform"):
  with members:
    - "matrix coherence and the nystrom method"
    - "near optimal coresets for least squares regression"
    - "low rank approximation and regression in input sparsity time"
    - "recovering low rank matrices from few coefficients in any basis"
    - "near optimal column based matrix reconstruction"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "random projections for k means clustering"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "faster subset selection for matrices and applications"):
  with members:
    - "deterministic feature selection for k means clustering"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "on truncated svd like sparse solutions to least squares problems of arbitrary dimensions"
    - "fast approximation of matrix coherence and statistical leverage"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "exponential inapproximability of selecting a maximum volume sub matrix"
    - "efficient volume sampling for row column subset selection"

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)