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: "analysis and improvement of low rank representation for subspace segmentation"
  Abstract: We analyze and improve low rank representation (LRR), the state-of-the-art algorithm for subspace segmentation of data. We prove that for the noiseless case, the optimization model of LRR has a unique solution, which is the shape interaction matrix (SIM) of the data matrix. So in essence LRR is equivalent to factorization methods. We also prove that the minimum value of the optimization model of LRR is equal to the rank of the data matrix. For the noisy case, we show that LRR can be approximated as a factorization method that combines noise removal by column sparse robust PCA. We further propose an improved version of LRR, called Robust Shape Interaction (RSI), which uses the corrected data as the dictionary instead of the noisy data. RSI is more robust than LRR when the corruption in data is heavy. Experiments on both synthetic and real data testify to the improved robustness of RSI.

Connected through hyperedge HE-1 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering"):
  with members:
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-2 (induced by source paper "feature concatenation multi view subspace clustering"):
  with members:
    - "multi view low rank sparse subspace clustering"
    - "discriminative block diagonal representation learning for image recognition"
    - "a survey on multi view clustering"
    - "visual understanding via multi feature shared learning with global consistency"
    - "iterative views agreement an iterative low rank based structured optimization method to multi view spectral clustering"
    - "a survey on multi view learning"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-3 (induced by source paper "subspace clustering using a symmetric low rank representation"):
  with members:
    - "automatic subspace learning via principal coefficients embedding"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "a unified framework for representation based subspace clustering of out of sample and large scale data"
    - "symmetric low rank representation for subspace clustering"
    - "fast low rank representation based spatial pyramid matching for image classification"
    - "correntropy induced l2 graph for robust subspace clustering"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-4 (induced by source paper "collaborative low rank subspace clustering"):
  with members:
    - "robust subspace clustering"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"
    - "building high level features using large scale unsupervised learning"
    - "robust principal component analysis"

Connected through hyperedge HE-5 (induced by source paper "symmetric low rank representation for subspace clustering"):
  with members:
    - "subspace clustering using a symmetric low rank representation"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "greedy feature selection for subspace clustering"
    - "generalized principal component analysis gpca"
    - "fixed rank representation for unsupervised visual learning"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (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"
    - "exact subspace segmentation and outlier detection by low rank representation"
    - "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"

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.CV(Computer Vision and Pattern Recognition)