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: "recursive robust pca or recursive sparse recovery in large but structured noise"
  Abstract: This work studies the recursive robust principal components' analysis(PCA) problem. Here, "robust" refers to robustness to both independent and correlated sparse outliers. If the outlier is the signal-of-interest, this problem can be interpreted as one of recursively recovering a time sequence of sparse vectors, St, in the presence of large but structured noise, Lt. The structure that we assume on Lt is that Lt is dense and lies in a low dimensional subspace that is either fixed or changes "slowly enough". A key application where this problem occurs is in video surveillance where the goal is to separate a slowly changing background (Lt) from moving foreground objects (St) on-the-fly. To solve the above problem, we introduce a novel solution called Recursive Projected CS (ReProCS). Under mild assumptions, we show that, with high probability (w.h.p.), ReProCS 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 at all times.

Connected through hyperedge HE-1 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "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"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-2 (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-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 "simple and practical algorithms for ell_p norm low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "a general and adaptive robust loss function"
    - "low rank matrix approximation in the infinity norm"
    - "algorithms for ell_p low rank approximation"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "recovery guarantee of non negative matrix factorization via alternating updates"
    - "correlation clustering with low rank matrices"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-6 (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"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "stable principal component pursuit"
    - "real time robust principal components pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "phaseless subspace tracking"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "gespar efficient phase retrieval of sparse signals"
    - "recovery of sparse 1 d signals from the magnitudes of their fourier transform"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of robust pca and l1 norm low rank matrix approximation"):
  with members:
    - "input sparsity and hardness for robust subspace approximation"
    - "robust pca via outlier pursuit"
    - "nuclear norm minimization for the planted clique and biclique problems"
    - "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)