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: "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
  Abstract: This paper is concerned with the problem of low-rank plus sparse matrix decomposition for big data. Conventional algorithms for matrix decomposition use the entire data to extract the low-rank and sparse components, and are based on optimization problems that scale with the dimension of the data, which limit their scalability. Furthermore, the existing randomized approaches mostly rely on uniform random sampling, which can be quite inefficient for many real world data matrices that exhibit additional structures (e.g. clustering). In this paper, a scalable subspace-pursuit approach that transforms the decomposition problem to a subspace learning problem is proposed. The decomposition is carried out using a small data sketch formed from sampled columns/rows. Even when the data is sampled uniformly at random, it is shown that the sufficient number of sampled columns/rows is roughly O(r \mu), where \mu is the coherency parameter and r the rank of the low-rank component. In addition, efficient sampling algorithms are proposed to address the problem of column/row sampling from structured data. The proposed sampling algorithms can be independently used for feature selection from high-dimensional data. The proposed approach is amenable to online implementation and an online scheme is proposed.

Connected through hyperedge HE-1 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "fast algorithms for robust pca via gradient descent"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "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-2 (induced by source paper "innovation pursuit a new approach to subspace clustering"):
  with members:
    - "a direction search and spectral clustering based approach to subspace clustering"
    - "spatial random sampling a structure preserving data sketching tool"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "robust computation of linear models by convex relaxation"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-3 (induced by source paper "compressive pca for low rank matrices on graphs"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "stationary signal processing on graphs"
    - "fast robust pca on graphs"
    - "network topology identification from spectral templates"
    - "compressive spectral clustering"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "robust principal component analysis on graphs"

Connected through hyperedge HE-4 (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"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "dissimilarity based sparse subset selection"

Connected through hyperedge HE-5 (induced by source paper "sparsity based error detection in dc power flow state estimation"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"
    - "distributed security constrained economic dispatch"

Connected through hyperedge HE-6 (induced by source paper "screen content image segmentation using sparse smooth decomposition"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "iris recognition using scattering transform and textural features"
    - "fingerprint recognition using translation invariant scattering network"
    - "screen content image segmentation using least absolute deviation fitting"
    - "multispectral palmprint recognition using textural features"
    - "a robust regression approach for background foreground segmentation"

Connected through hyperedge HE-7 (induced by source paper "a robust regression approach for background foreground segmentation"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "multispectral palmprint recognition using a hybrid feature"

Connected through hyperedge HE-8 (induced by source paper "a subspace method for array covariance matrix estimation"):
  with members:
    - "innovation pursuit a new approach to subspace clustering"

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)