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: "principal component pursuit with reduced linear measurements"
  Abstract: In this paper, we study the problem of decomposing a superposition of a low-rank matrix and a sparse matrix when a relatively few linear measurements are available. This problem arises in many data processing tasks such as aligning multiple images or rectifying regular texture, where the goal is to recover a low-rank matrix with a large fraction of corrupted entries in the presence of nonlinear domain transformation. We consider a natural convex heuristic to this problem which is a variant to the recently proposed Principal Component Pursuit. We prove that under suitable conditions, this convex program guarantees to recover the correct low-rank and sparse components despite reduced measurements. Our analysis covers both random and deterministic measurement models.

Connected through hyperedge HE-1 (induced by source paper "robust pca with partial subspace knowledge"):
  with members:
    - "a correctness result for online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "prost a smoothed lp norm robust online subspace tracking method for realtime background subtraction in video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "compressive principal component pursuit"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"

Connected through hyperedge HE-2 (induced by source paper "an online algorithm for separating sparse and low dimensional signal sequences from their sum"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "recursive sparse recovery in large but structured noise part 2"
    - "estimation of simultaneously sparse and low rank matrices"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "reprocs a missing link between recursive robust pca and recursive sparse recovery in large but correlated noise"

Connected through hyperedge HE-3 (induced by source paper "estimating traffic and anomaly maps via network tomography"):
  with members:
    - "rank regularization and bayesian inference for tensor completion and extrapolation"
    - "decentralized sparsity regularized rank minimization algorithms and applications"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "recursive sparse recovery in large but structured noise part 2"):
  with members:
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "estimation of simultaneously sparse and low rank matrices"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "robust pca via outlier pursuit"
    - "real time robust principal components pursuit"
    - "modified cs modifying compressive sensing for problems with partially known support"

Connected through hyperedge HE-5 (induced by source paper "recursive robust pca or recursive sparse recovery in large but structured noise"):
  with members:
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"
    - "recursive sparse recovery in large but structured noise part 2"
    - "estimation of simultaneously sparse and low rank matrices"
    - "sharp recovery bounds for convex demixing with applications"
    - "compressive principal component pursuit"
    - "support predicted modified cs for recursive robust principal components pursuit"

Connected through hyperedge HE-6 (induced by source paper "compressive principal component pursuit"):
  with members:
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovering low rank matrices from few coefficients in any basis"
    - "low rank matrix recovery from errors and erasures"
    - "strongly convex programming for exact matrix completion and robust principal component analysis"
    - "stable principal component pursuit"
    - "robust pca via outlier pursuit"
    - "dense error correction for low rank matrices via principal component pursuit"
    - "structured sparsity inducing norms through submodular functions"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing of simultaneous low rank and joint sparse matrices"):
  with members:
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "compressive music a missing link between compressive sensing and array signal processing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "matrix completion from a few entries"
    - "matrix completion with 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.IT(Information Theory)