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: "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
  Abstract: The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more rows than columns, so-called “tall-and-skinny matrices,” there is a numerically stable, efficient, communication-avoiding algorithm for computing the QR factorization. It has been used in traditional high performance computing and grid computing environments. For MapReduce environments, existing methods to compute the QR decomposition use a numerically unstable approach that relies on indirectly computing the Q factor. In the best case, these methods require only two passes over the data. In this paper, we describe how to compute a stable tall-and-skinny QR factorization on a MapReduce architecture in only slightly more than 2 passes over the data. We can compute the SVD with only a small change and no difference in performance. We present a performance comparison between our new direct TSQR method, indirect TSQR methods that use the communication-avoiding TSQR algorithm, and a standard unstable implementation for MapReduce (Cholesky QR). We find that our new stable method is competitive with unstable methods for matrices with a modest number of columns. This holds both in a theoretical performance model as well as in an actual implementation.

Connected through hyperedge HE-1 (induced by source paper "compressed nonnegative matrix factorization is fast and accurate"):
  with members:
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "the fast cauchy transform and faster robust linear regression"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-2 (induced by source paper "randomized algorithms for distributed computation of principal component analysis and singular value decomposition"):
  with members:
    - "fast and rip optimal transforms"

Connected through hyperedge HE-3 (induced by source paper "optimal principal component analysis in distributed and streaming models"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "tighter low rank approximation via sampling the leveraged element"
    - "relative errors for deterministic low rank matrix approximations"

Connected through hyperedge HE-4 (induced by source paper "matrix computations and optimization in apache spark"):
  with members:
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-5 (induced by source paper "era of big data processing a new approach via tensor networks and tensor decompositions"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "overview of constrained parafac models"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

Connected through hyperedge HE-6 (induced by source paper "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"):
  with members:
    - "computing a nonnegative matrix factorization provably"
    - "qr factorization of tall and skinny matrices in a grid computing environment"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "dimension independent matrix square using mapreduce"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "dimension independent similarity computation"
    - "fast approximation of matrix coherence and statistical leverage"
    - "faster least squares approximation"

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.DC(Distributed, Parallel, and Cluster Computing)