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 "static and dynamic robust pca and matrix completion a review"):
  Abstract: Principal component analysis (PCA) is one of the most widely used dimension reduction techniques. Robust PCA (RPCA) refers to the problem of PCA when the data may be corrupted by outliers. Recent work by Cands, Wright, Li, and Ma defined RPCA as a problem of decomposing a given data matrix into the sum of a low-rank matrix (true data) and a sparse matrix (outliers). The column space of the low-rank matrix then gives the PCA solution. This simple definition has led to a large amount of interesting new work on provably correct, fast, and practical solutions to RPCA. More recently, the dynamic (time-varying) version of the RPCA problem has been studied and a series of provably correct, fast, and memory-efficient tracking solutions have been proposed. Dynamic RPCA [or robust subspace tracking RST)] is the problem of tracking data lying in a (slowly) changing subspace, while being robust to sparse outliers. This paper provides an exhaustive review of the last decade of literature on RPCA and its dynamic counterpart (RST), along with describing their theoretical guarantees, discussing the pros and cons of various approaches, and providing empirical comparisons of performance and speed. A brief overview of the newer (lowrank) matrix completion (MC) literature is also provided. This refers to the problem of completing a low-rank matrix when only a subset of its entries are observed. It can be interpreted as a simpler special case of RPCA in which the indices of the outlier corrupted entries are known.

Includes members:
  Member "provable dynamic robust pca or robust subspace tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provable low rank phase retrieval and compressive pca")
  Member "an overview of robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "video denoising via dynamic video layering":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "static and dynamic robust pca via low rank sparse matrix decomposition a review")
  Member "robust subspace learning robust pca robust subspace tracking and robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "low rank phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "phase retrieval of low rank matrices by anchored regression")
  Member "nearly optimal robust subspace tracking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "federated over the air subspace learning from incomplete data")
  Member "no spurious local minima in nonconvex low rank problems a unified geometric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "fast algorithms for robust pca via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems")

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)