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: "randomized block krylov methods for stronger and faster approximate singular value decomposition"
  Abstract: Since being analyzed by Rokhlin, Szlam, and Tygert and popularized by Halko, Martinsson, and Tropp, randomized Simultaneous Power Iteration has become the method of choice for approximate singular value decomposition. It is more accurate than simpler sketching algorithms, yet still converges quickly for any matrix, independently of singular value gaps. After $\tilde{O}(1/\epsilon)$ iterations, it gives a low-rank approximation within $(1+\epsilon)$ of optimal for spectral norm error. #R##N#We give the first provable runtime improvement on Simultaneous Iteration: a simple randomized block Krylov method, closely related to the classic Block Lanczos algorithm, gives the same guarantees in just $\tilde{O}(1/\sqrt{\epsilon})$ iterations and performs substantially better experimentally. Despite their long history, our analysis is the first of a Krylov subspace method that does not depend on singular value gaps, which are unreliable in practice. #R##N#Furthermore, while it is a simple accuracy benchmark, even $(1+\epsilon)$ error for spectral norm low-rank approximation does not imply that an algorithm returns high quality principal components, a major issue for data applications. We address this problem for the first time by showing that both Block Krylov Iteration and a minor modification of Simultaneous Iteration give nearly optimal PCA for any matrix. This result further justifies their strength over non-iterative sketching methods. #R##N#Finally, we give insight beyond the worst case, justifying why both algorithms can run much faster in practice than predicted. We clarify how simple techniques can take advantage of common matrix properties to significantly improve runtime.

Connected through hyperedge HE-1 (induced by source paper "homogeneous network embedding for massive graphs via reweighted personalized pagerank"):
  with members:
    - "adversarial training methods for network embedding"
    - "graphvite a high performance cpu gpu hybrid system for node embedding"
    - "netsmf large scale network embedding as sparse matrix factorization"
    - "efficient algorithms for approximate single source personalized pagerank queries"
    - "pytorch biggraph a large scale graph embedding system"
    - "scalable graph embeddings via sparse transpose proximities"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"

Connected through hyperedge HE-2 (induced by source paper "fisher bures adversary graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "simplifying graph convolutional networks"
    - "fisher information and natural gradient learning of random deep networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "pitfalls of graph neural network evaluation"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-3 (induced by source paper "streaming batch eigenupdates for hardware neural networks"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "the fast convergence of incremental pca"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-4 (induced by source paper "the gradient complexity of linear regression"):
  with members:
    - "querying a matrix through matrix vector products"
    - "on the randomized complexity of minimizing a convex quadratic function"
    - "the noisy power method a meta algorithm with applications"

Connected through hyperedge HE-5 (induced by source paper "enhanced parallelization of the incremental 4d var data assimilation algorithm using the randomized incremental optimal technique riot"):
  with members:
    - "a time parallel approach to strong constraint four dimensional variational data assimilation"

Connected through hyperedge HE-6 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  with members:
    - "solving ridge regression using sketched preconditioned svrg"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "faster sgd using sketched conditioning"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "randomized sketches of convex programs with sharp guarantees"
    - "mini batch primal and dual methods for svms"
    - "recovering the optimal solution by dual random projection"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-7 (induced by source paper "tight query complexity lower bounds for pca via finite sample deformed wigner law"):
  with members:
    - "optimal lower bounds for universal relation samplers and finding duplicates"
    - "lifting randomized query complexity to randomized communication complexity"
    - "how to escape saddle points efficiently"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "lower bounds for adaptive sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "exploiting numerical sparsity for efficient learning faster eigenvector computation and regression"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "a note on randomized element wise matrix sparsification"
    - "near optimal entrywise sampling for data matrices"

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)