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: "solving principal component pursuit in linear time via l_1 filtering"
  Abstract: In the past decades, exactly recovering the intrinsic data structure from corrupted observations, which is known as robust principal component analysis (RPCA), has attracted tremendous interests and found many applications in computer vision. Recently, this problem has been formulated as recovering a low-rank component and a sparse component from the observed data matrix. It is proved that under some suitable conditions, this problem can be exactly solved by principal component pursuit (PCP), i.e., minimizing a combination of nuclear norm and $l_1$ norm. Most of the existing methods for solving PCP require singular value decompositions (SVD) of the data matrix, resulting in a high computational complexity, hence preventing the applications of RPCA to very large scale computer vision problems. In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$. Moreover, $l_1$ filtering is \emph{highly parallelizable}. It is the first algorithm that can \emph{exactly} solve a nuclear norm minimization problem in \emph{linear time} (with respect to the data size). Experiments on both synthetic data and real applications testify to the great advantage of $l_1$ filtering in speed over state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "fast randomized algorithms for t product based tensor operations and decompositions with applications to imaging data"):
  with members:
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "tensor sparse and low rank based submodule clustering method for multi way data"
    - "on unifying multi view self representations for clustering by tensor multi rank minimization"
    - "exact tensor completion using t svd"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "clustering multi way data a novel algebraic approach"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-2 (induced by source paper "high dimensional low rank plus sparse matrix decomposition"):
  with members:
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"
    - "compressive principal component pursuit"
    - "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-3 (induced by source paper "robust and scalable column row sampling from corrupted big data"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "spatial random sampling a structure preserving data sketching tool"
    - "innovation pursuit a new approach to subspace clustering"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "screen content image segmentation using robust regression and sparse decomposition"
    - "screen content image segmentation using sparse decomposition and total variation minimization"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"

Connected through hyperedge HE-4 (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"
    - "performance guarantees for reprocs correlated low rank matrix entries case"
    - "a subspace method for array covariance matrix estimation"
    - "an online algorithm for separating sparse and low dimensional signal sequences from their sum"

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.NA(Numerical Analysis)