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: "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
  Abstract: Numerous algorithms are used for nonnegative matrix factorization under the assumption that the matrix is nearly separable. In this paper, we show how to make these algorithms efficient for data matrices that have many more rows than columns, so-called "tall-and-skinny matrices". One key component to these improved methods is an orthogonal matrix transformation that preserves the separability of the NMF problem. Our final methods need a single pass over the data matrix and are suitable for streaming, multi-core, and MapReduce architectures. We demonstrate the efficacy of these algorithms on terabyte-sized synthetic matrices and real-world matrices from scientific computing and bioinformatics.

Connected through hyperedge HE-1 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-2 (induced by source paper "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"):
  with members:
    - "hierarchical clustering of hyperspectral images using rank two nonnegative matrix factorization"
    - "a high performance parallel algorithm for nonnegative matrix factorization"
    - "mllib machine learning in apache spark"
    - "matrix computations and optimization in apache spark"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "randomized algorithms for matrices and data"
    - "block locally optimal preconditioned eigenvalue xolvers blopex in hypre and petsc"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-3 (induced by source paper "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"):
  with members:
    - "a high performance parallel algorithm for nonnegative matrix factorization"
    - "mllib machine learning in apache spark"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"
    - "tensor decompositions for learning latent variable models"
    - "descent methods for nonnegative matrix factorization"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-4 (induced by source paper "how to fake multiply by a gaussian matrix"):
  with members:
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "sketching as a tool for numerical linear algebra"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random projections for linear support vector machines"
    - "low rank approximation and regression in input sparsity time"
    - "computing a nonnegative matrix factorization provably"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "compressed nonnegative matrix factorization is fast and accurate"):
  with members:
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "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-6 (induced by source paper "learning mixed membership mallows models from pairwise comparisons"):
  with members:
    - "learning mixtures of ranking models"
    - "a topic modeling approach to ranking"
    - "a practical algorithm for topic modeling with provable guarantees"

Connected through hyperedge HE-7 (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"
    - "direct qr factorizations for tall and skinny matrices in mapreduce architectures"
    - "higher order partial least squares hopls a generalized multilinear regression method"
    - "low complexity damped gauss newton algorithms for candecomp parafac"

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)