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: "row sampling for matrix algorithms via a non commutative bernstein bound"
  Abstract: We focus the use of \emph{row sampling} for approximating matrix algorithms. We give applications to matrix multipication; sparse matrix reconstruction; and, \math{\ell_2} regression. For a matrix \math{\matA\in\R^{m\times d}} which represents \math{m} points in \math{d\ll m} dimensions, all of these tasks can be achieved in \math{O(md^2)} via the singular value decomposition (SVD). For appropriate row-sampling probabilities (which typically depend on the norms of the rows of the \math{m\times d} left singular matrix of \math{\matA} (the \emph{leverage scores}), we give row-sampling algorithms with linear (up to polylog factors) dependence on the stable rank of \math{\matA}. This result is achieved through the application of non-commutative Bernstein bounds. #R##N#We then give, to our knowledge, the first algorithms for computing approximations to the appropriate row-sampling probabilities without going through the SVD of \math{\matA}. Thus, these are the first \math{o(md^2)} algorithms for row-sampling based approximations to the matrix algorithms which use leverage scores as the sampling probabilities. The techniques we use to approximate sampling according to the leverage scores uses some powerful recent results in the theory of random projections for embedding, and may be of some independent interest. We confess that one may perform all these matrix tasks more efficiently using these same random projection methods, however the resulting algorithms are in terms of a small number of linear combinations of all the rows. In many applications, the actual rows of \math{\matA} have some physical meaning and so methods based on a small number of the actual rows are of interest.

Connected through hyperedge HE-1 (induced by source paper "fast fixed dimension l2 subspace embeddings of arbitrary accuracy with application to l1 and l2 tasks"):
  with members:
    - "fast and rip optimal transforms"
    - "quantile regression for large scale applications"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"

Connected through hyperedge HE-2 (induced by source paper "testing matrix rank optimally"):
  with members:
    - "improved algorithms for adaptive compressed sensing"
    - "matrix completion and related problems via strong duality"
    - "embeddings of schatten norms with applications to data streams"
    - "on approximating functions of the singular values in a stream"
    - "the dictionary testing problem"
    - "sublinear estimation of weighted matchings in dynamic data streams"
    - "sketching as a tool for numerical linear algebra"
    - "relations among some low rank subspace recovery models"

Connected through hyperedge HE-3 (induced by source paper "active regression via linear sample sparsification"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "tail bounds for volume sampled linear regression"
    - "relative error tensor low rank approximation"
    - "unbiased estimates for linear regression via volume sampling"
    - "fourier sparse interpolation without a frequency gap"
    - "convergence rates of active learning for maximum likelihood estimation"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-4 (induced by source paper "sketching as a tool for numerical linear algebra"):
  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"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "subspace embeddings and ell_p regression using exponential random variables"

Connected through hyperedge HE-5 (induced by source paper "optimal cur matrix decompositions"):
  with members:
    - "revisiting the nystrom method for improved large scale machine learning"
    - "relative errors for deterministic low rank matrix approximations"
    - "principal component analysis and higher correlations for distributed data"
    - "low rank approximation and regression in input sparsity time"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "optimal column based low rank matrix reconstruction"

Connected through hyperedge HE-6 (induced by source paper "deterministic feature selection for k means clustering"):
  with members:
    - "a simple d 2 sampling based ptas for k means and other clustering problems"
    - "near optimal column based matrix reconstruction"
    - "random projections for k means clustering"
    - "twice ramanujan sparsifiers"
    - "relative error cur matrix decompositions"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-7 (induced by source paper "iterative row sampling"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "the fast cauchy transform and faster robust linear regression"
    - "effective stiffness generalizing effective resistance sampling to finite element matrices"
    - "near optimal column based matrix reconstruction"
    - "sublinear optimization for machine learning"

Connected through hyperedge HE-8 (induced by source paper "topics in matrix sampling algorithms"):
  with members:
    - "a matrix hyperbolic cosine algorithm and applications"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "a nearly mlogn time solver for sdd linear systems"
    - "a unified framework for approximating and clustering data"
    - "lasserre hierarchy higher eigenvalues and approximation schemes for quadratic integer programming with psd objectives"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "random projections for k means clustering"

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)