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: "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
  Abstract: This paper designs and extensively evaluates an online algorithm, called practical recursive projected compressive sensing (Prac-ReProCS), for recovering a time sequence of sparse vectors St and a time sequence of dense vectors L#N# t#N# from their sum, M#N# t#N#: = S#N# t#N# + L#N# t#N#, when the L#N# t#N#'s lie in a slowly changing low-dimensional subspace of the full space. A key application where this problem occurs is in real-time video layering where the goal is to separate a video sequence into a slowly changing background sequence and a sparse foreground sequence that consists of one or more moving regions/objects on-the-fly. Prac-ReProCS is a practical modification of its theoretical counterpart which was analyzed in our recent work. Extension to the undersampled case is also developed. Extensive experimental comparisons demonstrating the advantage of the approach for both simulated and real videos, over existing batch and recursive methods, are shown.

Connected through hyperedge HE-1 (induced by source paper "provable dynamic robust pca or robust subspace tracking"):
  with members:
    - "static and dynamic robust pca and matrix completion a review"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "nearly optimal robust subspace tracking"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"
    - "non convex robust pca"

Connected through hyperedge HE-2 (induced by source paper "static and dynamic robust pca and matrix completion a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "video denoising via dynamic video layering"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-3 (induced by source paper "static and dynamic robust pca via low rank sparse matrix decomposition a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "video denoising via dynamic video layering"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-4 (induced by source paper "video denoising via dynamic video layering"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "fast algorithms for robust pca via gradient descent"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "stable principal component pursuit"
    - "real time robust principal components pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "weighted low rank approximation of matrices and background modeling"):
  with members:
    - "background subtraction via generalized fused lasso foreground modeling"
    - "towards benchmarking scene background initialization"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "support predicted modified cs for recursive robust principal components pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "online decomposition of compressive streaming data using n ell_1 cluster weighted minimization"):
  with members:
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "approximation algorithms for model based compressive sensing"
    - "compressive principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-7 (induced by source paper "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "robust online matrix factorization for dynamic background subtraction"
    - "fast algorithms for robust pca via gradient descent"
    - "correlated pca principal components analysis when data and noise are correlated"
    - "nearly optimal robust matrix completion"

Connected through hyperedge HE-8 (induced by source paper "high dimensional low rank plus sparse matrix decomposition"):
  with members:
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "compressive principal component pursuit"
    - "solving principal component pursuit in linear time via l_1 filtering"
    - "randomized algorithms for matrices and data"
    - "stable principal component pursuit"
    - "efficient volume sampling for row column subset selection"
    - "robust principal component analysis"

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)