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: "correlated pca principal components analysis when data and noise are correlated"
  Abstract: Given a matrix of observed data, Principal Components Analysis (PCA) computes a small number of orthogonal directions that contain most of its variability. Provably accurate solutions for PCA have been in use for a long time. However, to the best of our knowledge, all existing theoretical guarantees for it assume that the data and the corrupting noise are mutually independent, or at least uncorrelated. This is valid in practice often, but not always. In this paper, we study the PCA problem in the setting where the data and noise can be correlated. Such noise is often also referred to as "data-dependent noise". We obtain a correctness result for the standard eigenvalue decomposition (EVD) based solution to PCA under simple assumptions on the data-noise correlation. We also develop and analyze a generalization of EVD, cluster-EVD, that improves upon EVD in certain regimes.

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"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"
    - "non convex robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"

Connected through hyperedge HE-2 (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-3 (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"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-4 (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"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"

Connected through hyperedge HE-5 (induced by source paper "low rank phase retrieval"):
  with members:
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phase retrieval with application to optical imaging"
    - "fast matrix completion without the condition number"
    - "gespar efficient phase retrieval of sparse signals"
    - "phase retrieval stability and recovery guarantees"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

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"
    - "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"
    - "robust pca with partial subspace knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"

Connected through hyperedge HE-7 (induced by source paper "pca in data dependent noise correlated pca nearly optimal finite sample guarantees"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "nearly optimal robust matrix completion"
    - "online matrix completion and online robust pca"
    - "non convex robust pca"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "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.LG(Machine Learning)