human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "innovation pursuit a new approach to subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "minimal sample subspace learning theory and algorithms")
  Member "screen content image segmentation using sparse smooth decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "enhanced signal recovery via sparsity inducing image priors")
  Member "identifying outliers in large matrices via randomized adaptive compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks")
  Member "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "moving objects detection with a moving camera a comprehensive review")
  Member "screen content image segmentation using least absolute deviation fitting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enhanced signal recovery via sparsity inducing image priors")
  Member "performance guarantees for reprocs correlated low rank matrix entries case":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high dimensional low rank plus sparse matrix decomposition")
  Member "a subspace method for array covariance matrix estimation":
    also connected through other hyperedges:
      (none)
  Member "an online algorithm for separating sparse and low dimensional signal sequences from their sum":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable dynamic robust pca or robust subspace tracking")

Question: Please tell me which class the source paper 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)