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 "robust pca with partial subspace knowledge"):
  Abstract: In recent work, robust Principal Components Analysis (PCA) has been posed as a problem of recovering a low-rank matrix $\mathbf{L}$ and a sparse matrix $\mathbf{S}$ from their sum, $\mathbf{M}:= \mathbf{L} + \mathbf{S}$ and a provably exact convex optimization solution called PCP has been proposed. This work studies the following problem. Suppose that we have partial knowledge about the column space of the low rank matrix $\mathbf{L}$. Can we use this information to improve the PCP solution, i.e. allow recovery under weaker assumptions? We propose here a simple but useful modification of the PCP idea, called modified-PCP, that allows us to use this knowledge. We derive its correctness result which shows that, when the available subspace knowledge is accurate, modified-PCP indeed requires significantly weaker incoherence assumptions than PCP. Extensive simulations are also used to illustrate this. Comparisons with PCP and other existing work are shown for a stylized real application as well. Finally, we explain how this problem naturally occurs in many applications involving time series data, i.e. in what is called the online or recursive robust PCA problem. A corollary for this case is also given.

Includes members:
  Member "a correctness result for online robust pca":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online and offline robust pca novel algorithms and performance guarantees")
  Member "an online algorithm for separating sparse and low dimensional signal sequences from their sum":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provable dynamic robust pca or robust subspace tracking")
  Member "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "dynamic anomalography tracking network anomalies via sparsity and low rank":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "static and dynamic robust pca and matrix completion a review")
  Member "recursive robust pca or recursive sparse recovery in large but structured noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "compressive principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "an online algorithm for separating sparse and low dimensional signal sequences from their sum")
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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)