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: "robust and efficient subspace segmentation via least squares regression"
  Abstract: This paper studies the subspace segmentation problem which aims to segment data drawn from a union of multiple linear subspaces. Recent works by using sparse representation, low rank representation and their extensions attract much attention. If the subspaces from which the data drawn are independent or orthogonal, they are able to obtain a block diagonal affinity matrix, which usually leads to a correct segmentation. The main differences among them are their objective functions. We theoretically show that if the objective function satisfies some conditions, and the data are sufficiently drawn from independent subspaces, the obtained affinity matrix is always block diagonal. Furthermore, the data sampling can be insufficient if the subspaces are orthogonal. Some existing methods are all special cases. Then we present the Least Squares Regression (LSR) method for subspace segmentation. It takes advantage of data correlation, which is common in real data. LSR encourages a grouping effect which tends to group highly correlated data together. Experimental results on the Hopkins 155 database and Extended Yale Database B show that our method significantly outperforms state-of-the-art methods. Beyond segmentation accuracy, all experiments demonstrate that LSR is much more efficient.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of linear regressions in subexponential time via fourier moments"):
  with members:
    - "list decodable linear regression"
    - "em converges for a mixture of many linear regressions"
    - "list decodable learning via sum of squares"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "learning mixtures of linear regressions with nearly optimal complexity"
    - "hyperplane clustering via dual principal component pursuit"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"
    - "learning from untrusted data"

Connected through hyperedge HE-2 (induced by source paper "neural collaborative subspace clustering"):
  with members:
    - "scalable deep k subspace clustering"
    - "clustergan latent space clustering in generative adversarial networks"
    - "deep subspace clustering networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "adaptive low rank kernel subspace clustering"
    - "oracle based active set algorithm for scalable elastic net subspace clustering"
    - "kernel sparse subspace clustering on symmetric positive definite manifolds"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-3 (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 recovery of subspace structures by low rank representation"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"

Connected through hyperedge HE-4 (induced by source paper "structure learning with similarity preserving"):
  with members:
    - "multi view multiple clustering"
    - "similarity learning via kernel preserving embedding"
    - "deep k means jointly clustering with k means and learning representations"
    - "deep subspace clustering networks"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "top n recommender system via matrix completion"
    - "unsupervised deep embedding for clustering analysis"

Connected through hyperedge HE-5 (induced by source paper "self supervised convolutional subspace clustering network"):
  with members:
    - "constrained sparse subspace clustering with side information"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "deep sparse subspace clustering"
    - "deep subspace clustering networks"
    - "non linear subspace clustering with learned low rank kernels"
    - "ol e orthogonal low rank embedding a plug and play geometric loss for deep learning"
    - "adaptive low rank kernel subspace clustering"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"

Connected through hyperedge HE-6 (induced by source paper "unsupervised deep epipolar flow for stationary or dynamic scenes"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "optical flow in mostly rigid scenes"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-7 (induced by source paper "neither global nor local a hierarchical robust subspace clustering for image data"):
  with members:
    - "robust subspace clustering by cauchy loss function"
    - "subspace clustering by block diagonal representation"
    - "structured sparse subspace clustering a joint affinity learning and subspace clustering framework"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "learning transformations for clustering and classification"
    - "robust recovery of subspace structures by low rank representation"
    - "clustering on multi layer graphs via subspace analysis on grassmann manifolds"
    - "generalized principal component analysis gpca"

Connected through hyperedge HE-8 (induced by source paper "robust subspace clustering by cauchy loss function"):
  with members:
    - "multi view intact space learning"
    - "sce a manifold regularized set covering method for data partitioning"
    - "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"
    - "joint unsupervised learning of deep representations and image clusters"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "robust recovery of subspace structures by low rank representation"

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)