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 "recursive sparse recovery in large but structured noise part 2"):
  Abstract: We study the problem of recursively recovering a time sequence of sparse vectors, St, from measurements Mt := St + Lt that are corrupted by structured noise Lt which is dense and can have large magnitude. The structure that we require is that Lt should lie in a low dimensional subspace that is either fixed or changes "slowly enough"; and the eigenvalues of its covariance matrix are "clustered". We do not assume any model on the sequence of sparse vectors. Their support sets and their nonzero element values may be either independent or correlated over time (usually in many applications they are correlated). The only thing required is that there be some support change every so often. We introduce a novel solution approach called Recursive Projected Compressive Sensing with cluster-PCA (ReProCS-cPCA) that addresses some of the limitations of earlier work. Under mild assumptions, we show that, with high probability, ReProCS-cPCA can exactly recover the support set of St at all times; and the reconstruction errors of both St and Lt are upper bounded by a time-invariant and small value.

Includes members:
  Member "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust subspace clustering with compressed data")
  Member "estimation of simultaneously sparse and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social network fusion and mining a survey")
  Member "recursive robust pca or recursive sparse recovery in large but structured noise":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "sharp recovery bounds for convex demixing with applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion")
  Member "compressive principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "principal component pursuit with reduced linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust pca with partial subspace knowledge")
  Member "robust pca via outlier pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "real time robust principal components pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provable subspace tracking from missing data and matrix completion")

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)