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: "random projections for the nonnegative least squares problem"
  Abstract: Constrained least-squares regression problems, such as the Nonnegative Least Squares (NNLS) problem, where the variables are restricted to take only nonnegative values, often arise in applications. Motivated by the recent development of the fast Johnson-Lindestrauss transform, we present a fast random projection type approximation algorithm for the NNLS problem. Our algorithm employs a randomized Hadamard transform to construct a much smaller NNLS problem and solves this smaller problem using a standard NNLS solver. We prove that our approach finds a nonnegative solution vector that, with high probability, is close to the optimum nonnegative solution in a relative error approximation sense. We experimentally evaluate our approach on a large collection of term-document data and verify that it does offer considerable speedups without a significant loss in accuracy. Our analysis is based on a novel random projection type result that might be of independent interest. In particular, given a tall and thin matrix $\Phi \in \mathbb{R}^{n \times d}$ ($n \gg d$) and a vector $y \in \mathbb{R}^d$, we prove that the Euclidean length of $\Phi y$ can be estimated very accurately by the Euclidean length of $\tilde{\Phi}y$, where $\tilde{\Phi}$ consists of a small subset of (appropriately rescaled) rows of $\Phi$.

Connected through hyperedge HE-1 (induced by source paper "a performance based scheme for pricing resources in the cloud"):
  with members:
    - "a market for scheduling with applications to cloud computing"
    - "fixed and market pricing for cloud services"

Connected through hyperedge HE-2 (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"
    - "deterministic feature selection for k means clustering"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "near optimal coresets for least squares regression"):
  with members:
    - "principal component analysis and higher correlations for distributed data"
    - "topics in matrix sampling algorithms"
    - "near optimal column based matrix reconstruction"
    - "twice ramanujan sparsifiers"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-4 (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"
    - "topics in matrix sampling algorithms"
    - "near optimal column based matrix reconstruction"
    - "algorithmic and statistical perspectives on large scale data analysis"
    - "random projections for k means clustering"

Connected through hyperedge HE-5 (induced by source paper "efficient dimensionality reduction for canonical correlation analysis"):
  with members:
    - "matrix coherence and the nystrom method"
    - "low rank approximation and regression in input sparsity time"
    - "two step cca a new spectral method for estimating vector models of words"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "random projections for k means clustering"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (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)