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: "a preconditioned hybrid svd method for accurately computing singular triplets of large matrices"
  Abstract: The computation of a few singular triplets of large, sparse matrices is a challenging task, especially when the smallest magnitude singular values are needed in high accuracy. Most recent efforts try to address this problem through variations of the Lanczos bidiagonalization method, but they are still challenged even for medium matrix sizes due to the difficulty of the problem. We propose a novel SVD approach that can take advantage of preconditioning and of any well-designed eigensolver to compute both largest and smallest singular triplets. Accuracy and efficiency is achieved through a hybrid, two-stage meta-method, PHSVDS. In the first stage, PHSVDS solves the normal equations up to the best achievable accuracy. If further accuracy is required, the method switches automatically to an eigenvalue problem with the augmented matrix. Thus it combines the advantages of the two stages, faster convergence and accuracy, respectively. For the augmented matrix, solving the interior eigenvalue is facilitated by pr...

Connected through hyperedge HE-1 (induced by source paper "eigenrec generalizing puresvd for effective and efficient top n recommendations"):
  with members:
    - "unbiased learning to rank with biased feedback"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "recommendations as treatments debiasing learning and evaluation"
    - "feature based factorized bilinear similarity model for cold start top n item recommendation"
    - "challenging the long tail recommendation"

Connected through hyperedge HE-2 (induced by source paper "word mover s embedding from word2vec to document embedding"):
  with members:
    - "document similarity for texts of varying lengths via hidden topics"
    - "embedding syntax and semantics of prepositions via tensor decomposition"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "sentence state lstm for text representation"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "prepositions in context"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"

Connected through hyperedge HE-3 (induced by source paper "scalable spectral clustering using random binning features"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "random warping series a random features method for time series embedding"
    - "incremental eigenpair computation for graph laplacian matrices theory and applications"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "compressive spectral clustering"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-4 (induced by source paper "incremental eigenpair computation for graph laplacian matrices theory and applications"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "principled multilayer network embedding"
    - "name disambiguation in anonymized graphs using network embedding"
    - "phase transitions and a model order selection criterion for spectral graph clustering"
    - "nous construction and querying of dynamic knowledge graphs"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "trust from the past bayesian personalized ranking based link prediction in knowledge graphs"

Connected through hyperedge HE-5 (induced by source paper "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"):
  with members:
    - "primme_svds a high performance preconditioned svd solver for accurate large scale computations"
    - "improved bounds on sample size for implicit matrix trace estimators"

Connected through hyperedge HE-6 (induced by source paper "primme_svds a high performance preconditioned svd solver for accurate large scale computations"):
  with members:
    - "revisiting random binning features fast convergence and strong parallelizability"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"

Connected through hyperedge HE-7 (induced by source paper "filtrated algebraic subspace clustering"):
  with members:
    - "filtrated spectral algebraic subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "a tutorial on spectral 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.NA(Numerical Analysis)