human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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).

Includes members:
  Member "randomized algorithms for matrices and data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "a sparse johnson lindenstrauss transform":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the effectiveness of johnson lindenstrauss transform for high dimensional optimization with outliers")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

Question: Please tell me which class the source paper 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)