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: "improved analyses of the randomized power method and block lanczos method"
  Abstract: The power method and block Lanczos method are popular numerical algorithms for computing the truncated singular value decomposition (SVD) and eigenvalue decomposition problems. Especially in the literature of randomized numerical linear algebra, the power method is widely applied to improve the quality of randomized sketching, and relative-error bounds have been well established. Recently, Musco & Musco (2015) proposed a block Krylov subspace method that fully exploits the intermediate results of the power iteration to accelerate convergence. They showed spectral gap-independent bounds which are stronger than the power method by order-of-magnitude. This paper offers novel error analysis techniques and significantly improves the bounds of both the randomized power method and the block Lanczos method. This paper also establishes the first gap-independent bound for the warm-start block Lanczos method.

Connected through hyperedge HE-1 (induced by source paper "break the ceiling stronger multi scale deep graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "multi stage self supervised learning for graph convolutional networks"
    - "a comprehensive survey on graph neural networks"
    - "virtual adversarial training on graph convolutional networks in node classification"
    - "adaptive graph convolutional neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph partition neural networks for semi supervised classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-2 (induced by source paper "lectures on randomized numerical linear algebra"):
  with members:
    - "structural convergence results for low rank approximations from block krylov spaces"
    - "structural convergence results for approximation of dominant subspaces from block krylov spaces"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "near optimal column based matrix reconstruction"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-3 (induced by source paper "structural convergence results for low rank approximations from block krylov spaces"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "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"
    - "near optimal column based matrix reconstruction"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "faster least squares approximation"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-4 (induced by source paper "structural convergence results for approximation of dominant subspaces from block krylov spaces"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "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"
    - "near optimal column based matrix reconstruction"
    - "faster least squares approximation"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-5 (induced by source paper "faster low rank approximation using adaptive gap based preconditioning"):
  with members:
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "convergence of stochastic gradient descent for pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-6 (induced by source paper "the singular value decomposition applications and beyond"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient symmetric matrix sketching and the cur matrix decomposition"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "the modified nystrom method theories algorithms and extension"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "optimal cur matrix decompositions"
    - "dimensionality reduction for k means clustering and low rank 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.NA(Numerical Analysis)