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: "performance guarantees for reprocs correlated low rank matrix entries case"
  Abstract: Online or recursive robust PCA can be posed as a problem of recovering a sparse vector, $S_t$, and a dense vector, $L_t$, which lies in a slowly changing low-dimensional subspace, from $M_t:= S_t + L_t$ on-the-fly as new data comes in. For initialization, it is assumed that an accurate knowledge of the subspace in which $L_0$ lies is available. In recent works, Qiu et al proposed and analyzed a novel solution to this problem called recursive projected compressed sensing or ReProCS. In this work, we relax one limiting assumption of Qiu et al's result. Their work required that the $L_t$'s be mutually independent over time. However this is not a practical assumption, e.g., in the video application, $L_t$ is the background image sequence and one would expect it to be correlated over time. In this work we relax this and allow the $L_t$'s to follow an autoregressive model. We are able to show that under mild assumptions and under a denseness assumption on the unestimated part of the changed subspace, with high probability (w.h.p.), ReProCS can exactly recover the support set of $S_t$ at all times; the reconstruction errors of both $S_t$ and $L_t$ are upper bounded by a time invariant and small value; and the subspace recovery error decays to a small value within a finite delay of a subspace change time. Because the last assumption depends on an algorithm estimate, this result cannot be interpreted as a correctness result but only a useful step towards it.

Connected through hyperedge HE-1 (induced by source paper "high dimensional low rank plus sparse matrix decomposition"):
  with members:
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"
    - "solving principal component pursuit in linear time via l_1 filtering"
    - "randomized algorithms for matrices and data"
    - "stable principal component pursuit"
    - "efficient volume sampling for row column subset selection"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-3 (induced by source paper "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "screen content image segmentation using sparse smooth decomposition"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "screen content image segmentation using least absolute deviation fitting"
    - "a subspace method for array covariance matrix estimation"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"

Connected through hyperedge HE-4 (induced by source paper "online matrix completion and online robust pca"):
  with members:
    - "a correctness result for online robust pca"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "local convergence of an algorithm for subspace identification from partial data"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "low rank matrix recovery from errors and erasures"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "a correctness result for online robust pca"):
  with members:
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "robust pca with partial subspace knowledge"
    - "recursive robust pca or recursive sparse recovery in large but structured noise"
    - "sharp recovery bounds for convex demixing with applications"
    - "online robust subspace tracking from partial information"
    - "low rank matrix recovery from errors and erasures"
    - "robust pca via outlier pursuit"
    - "robust principal component analysis"

Connected through hyperedge HE-6 (induced by source paper "recursive robust pca or recursive sparse recovery in large but structured noise"):
  with members:
    - "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"
    - "principal component pursuit with reduced linear measurements"
    - "support predicted modified cs for recursive robust principal components pursuit"

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)