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: "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.

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 "manifold denoising by nonlinear robust principal component analysis"):
  with members:
    - "tracing liquid level and material boundaries in transparent vessels using the graph cut computer vision approach"
    - "using curvature to distinguish between surface reflections and vessel contents in computer vision based recognition of materials in transparent vessels"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "nearly optimal robust subspace tracking"):
  with members:
    - "provable dynamic robust pca or 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"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "non convex robust pca"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-7 (induced by source paper "compressive online robust principal component analysis with optical flow for video foreground background separation"):
  with members:
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "adaptive rate compressive sensing using side information"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "online forecasting matrix factorization"):
  with members:
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "online learning for time series prediction"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

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)