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: "exact subspace segmentation and outlier detection by low rank representation"
  Abstract: In this work, we address the following matrix recovery problem: suppose we are given a set of data points containing two parts, one part consists of samples drawn from a union of multiple subspaces and the other part consists of outliers. We do not know which data points are outliers, or how many outliers there are. The rank and number of the subspaces are unknown either. Can we detect the outliers and segment the samples into their right subspaces, efficiently and exactly? We utilize a so-called {\em Low-Rank Representation} (LRR) method to solve this problem, and prove that under mild technical conditions, any solution to LRR exactly recovers the row space of the samples and detect the outliers as well. Since the subspace membership is provably determined by the row space, this further implies that LRR can perform exact subspace segmentation and outlier detection, in an efficient way.

Connected through hyperedge HE-1 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "automatic subspace learning via principal coefficients embedding"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "robust subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "provable self representation based outlier detection in a union of subspaces"):
  with members:
    - "thresholding based efficient outlier robust pca"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "robust subspace clustering via tighter rank approximation"):
  with members:
    - "logdet rank minimization with application to subspace clustering"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "efficient sparse group feature selection via nonconvex optimization"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "robust subspace clustering via smoothed rank approximation"):
  with members:
    - "logdet rank minimization with application to subspace clustering"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-5 (induced by source paper "robust recovery of subspace structures by low rank representation"):
  with members:
    - "hybrid linear modeling via local best fit flats"
    - "a geometric analysis of subspace clustering with outliers"
    - "analysis and improvement of low rank representation for subspace segmentation"
    - "robust pca via outlier pursuit"
    - "matrix completion from noisy entries"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "median k flats for hybrid linear modeling with many outliers"

Connected through hyperedge HE-6 (induced by source paper "distributed low rank subspace segmentation"):
  with members:
    - "generalized principal component analysis gpca"
    - "a simpler approach to matrix completion"
    - "robust principal component analysis"

Connected through hyperedge HE-7 (induced by source paper "fixed rank representation for unsupervised visual learning"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "a geometric analysis of subspace clustering with outliers"
    - "robust low rank subspace segmentation with semidefinite guarantees"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "a geometric analysis of subspace clustering with outliers"):
  with members:
    - "generalized principal component analysis gpca"
    - "hybrid linear modeling via local best fit flats"
    - "randomized hybrid linear modeling by local best fit flats"
    - "median k flats for hybrid linear modeling with many outliers"

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)