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: "deterministic feature selection for k means clustering"
  Abstract: We study feature selection for k-means clustering. Although the literature contains many methods with good empirical performance, algorithms with provable theoretical behavior have only recently been developed. Unfortunately, these algorithms are randomized and fail with, say, a constant probability. We present the first deterministic feature selection algorithm for k-means clustering with relative error guarantees. At the heart of our algorithm lies a deterministic method for decompositions of the identity and a structural result which quantifies some of the tradeoffs in dimensionality reduction.

Connected through hyperedge HE-1 (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"
    - "faster subset selection for matrices and applications"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-2 (induced by source paper "performance of johnson lindenstrauss transform for k means and k medians clustering"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "strong coresets for k median and subspace approximation goodbye dimension"
    - "optimality of the johnson lindenstrauss lemma"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "new constructions of rip matrices with fast multiplication and fewer rows"
    - "randomized dimensionality reduction for k means clustering"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-3 (induced by source paper "proportional volume sampling and approximation algorithms for a optimal design"):
  with members:
    - "unbiased estimates for linear regression via volume sampling"
    - "subsampling for ridge regression via regularized volume sampling"
    - "real stable polynomials and matroids optimization and counting"
    - "on largest volume simplices and sub determinants"
    - "randomized rounding for the largest simplex problem"
    - "efficient volume sampling for row column subset selection"

Connected through hyperedge HE-4 (induced by source paper "column selection via adaptive sampling"):
  with members:
    - "optimal sparse linear auto encoders and sparse pca"
    - "provable deterministic leverage score sampling"
    - "near optimal coresets for least squares regression"
    - "a note on sparse least squares regression"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "efficient volume sampling for row column subset selection"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-5 (induced by source paper "random projections for k means maintaining coresets beyond merge reduce"):
  with members:
    - "an algorithm for online k means clustering"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "low rank approximation and regression in input sparsity time"
    - "randomized dimensionality reduction for k means clustering"
    - "a unified framework for approximating and clustering data"
    - "a sparse johnson lindenstrauss transform"
    - "sparser johnson lindenstrauss transforms"
    - "rademacher chaos random eulerian graphs and the sparse johnson lindenstrauss transform"

Connected through hyperedge HE-6 (induced by source paper "dimensionality reduction for k means clustering and low rank approximation"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "improved distributed principal component analysis"
    - "tighter low rank approximation via sampling the leveraged element"
    - "uniform sampling for matrix approximation"
    - "relative errors for deterministic low rank matrix approximations"

Connected through hyperedge HE-7 (induced by source paper "spectral clustering via the power method provably"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "low rank approximation and regression in input sparsity time"
    - "efficient dimensionality reduction for canonical correlation analysis"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "random projections for k means clustering"
    - "approaching optimality for solving sdd systems"
    - "random projections for the nonnegative least squares problem"

Connected through hyperedge HE-8 (induced by source paper "faster subset selection for matrices and applications"):
  with members:
    - "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"
    - "topics in matrix sampling algorithms"
    - "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.LG(Machine Learning)