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: "sketched svd recovering spectral features from compressive measurements"
  Abstract: We consider a streaming data model in which n sensors observe individual streams of data, presented in a turnstile model. Our goal is to analyze the singular value decomposition (SVD) of the matrix of data defined implicitly by the stream of updates. Each column i of the data matrix is given by the stream of updates seen at sensor i. Our approach is to sketch each column of the matrix, forming a "sketch matrix" Y, and then to compute the SVD of the sketch matrix. We show that the singular values and right singular vectors of Y are close to those of X, with small relative error. We also believe that this bound is of independent interest in non-streaming and non-distributed data collection settings. #R##N#Assuming that the data matrix X is of size Nxn, then with m linear measurements of each column of X, we obtain a smaller matrix Y with dimensions mxn. If m = O(k \epsilon^{-2} (log(1/\epsilon) + log(1/\delta)), where k denotes the rank of X, then with probability at least 1-\delta, the singular values \sigma'_j of Y satisfy the following relative error result #R##N#(1-\epsilon)^(1/2)<= \sigma'_j/\sigma_j <= (1 + \epsilon)^(1/2) as compared to the singular values \sigma_j of the original matrix X. Furthermore, the right singular vectors v'_j of Y satisfy #R##N#||v_j-v_j'||_2 <= min(sqrt{2}, (\epsilon\sqrt{1+\epsilon})/(\sqrt{1-\epsilon}) max_{i\neq j} (\sqrt{2}\sigma_i\sigma_j)/(min_{c\in[-1,1]}(|\sigma^2_i-\sigma^2_j(1+c\epsilon)|))) as compared to the right singular vectors v_j of X. We apply this result to obtain a streaming graph algorithm to approximate the eigenvalues and eigenvectors of the graph Laplacian in the case where the graph has low rank (many connected components).

Connected through hyperedge HE-1 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"

Connected through hyperedge HE-2 (induced by source paper "compressed dynamic mode decomposition for background modeling"):
  with members:
    - "randomized low rank dynamic mode decomposition for motion detection"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "sketching as a tool for numerical linear algebra"
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "simple and deterministic matrix sketching"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "moses a streaming algorithm for linear dimensionality reduction"):
  with members:
    - "robust frequent directions with application in online learning"
    - "what to expect when you are expecting on the grassmannian"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "streaming principal component analysis from incomplete data"
    - "weighted matrix completion and recovery with prior subspace information"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "learning the differential correlation matrix of a smooth function from point samples"

Connected through hyperedge HE-4 (induced by source paper "fixed rank approximation of a positive semidefinite matrix from streaming data"):
  with members:
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "randomized single view algorithms for low rank matrix approximation"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-5 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "compressive principal component pursuit"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "algorithms and hardness for robust subspace recovery"
    - "sequential testing for sparse recovery"

Connected through hyperedge HE-6 (induced by source paper "compressed dynamic mode decomposition for real time object detection"):
  with members:
    - "randomized low rank dynamic mode decomposition for motion detection"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "using dynamic mode decomposition for real time background foreground separation in video"
    - "randomized algorithms for matrices and data"
    - "model based compressive sensing"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "subspace based low rank and joint sparse matrix recovery"):
  with members:
    - "compressed sensing of simultaneous low rank and joint sparse 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.IT(Information Theory)