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: "automatic subspace learning via principal coefficients embedding"
  Abstract: In this paper, we address two challenging problems in unsupervised subspace learning: 1) how to automatically identify the feature dimension of the learned subspace (i.e., automatic subspace learning) and 2) how to learn the underlying subspace in the presence of Gaussian noise (i.e., robust subspace learning). We show that these two problems can be simultaneously solved by proposing a new method [(called principal coefficients embedding (PCE)]. For a given data set    $\mathbf {D}\boldsymbol {\in } \mathbb{R}^{\boldsymbol {m\times n}}$   , PCE recovers a clean data set    $\mathbf {D}_{0}\boldsymbol {\in } \mathbb{R}^{\boldsymbol {m\times n}}$    from    $\mathbf {D}$    and simultaneously learns a global reconstruction relation    $\mathbf {C}\boldsymbol {\in } \mathbf {R}^{\boldsymbol {n\times n}}$    of    $\mathbf {D}_{\boldsymbol {0}}$   . By preserving    $\mathbf {C}$    into an    $\boldsymbol {m^{\prime }}$   -dimensional space, the proposed method obtains a projection matrix that can capture the latent manifold structure of    $\mathbf {D}_{\boldsymbol {0}}$   , where    $\boldsymbol {m^{\prime }\ll m}$    is automatically determined by the rank of    $\mathbf {C}$    with theoretical guarantees. PCE has three advantages: 1) it can automatically determine the feature dimension even though data are sampled from a union of multiple linear subspaces in presence of the Gaussian noise; 2) although the objective function of PCE only considers the Gaussian noise, experimental results show that it is robust to the non-Gaussian noise (e.g., random pixel corruption) and real disguises; and 3) our method has a closed-form solution and can be calculated very fast. Extensive experimental results show the superiority of PCE on a range of databases with respect to the classification accuracy, robustness, and efficiency.

Connected through hyperedge HE-1 (induced by source paper "robust subspace clustering with compressed data"):
  with members:
    - "subspace clustering by block diagonal representation"
    - "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"
    - "recovery of low rank plus compressed sparse matrices with application to unveiling traffic anomalies"

Connected through hyperedge HE-2 (induced by source paper "cost effective object detection active sample mining with switchable selection criteria"):
  with members:
    - "few example object detection with model communication"
    - "connections between nuclear norm and frobenius norm based representations"
    - "active self paced learning for cost effective and progressive face identification"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "cost effective active learning for deep image classification"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-3 (induced by source paper "connections between nuclear norm and frobenius norm based representations"):
  with members:
    - "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"
    - "learning locality constrained collaborative representation for robust face recognition"
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "collaborative representation based classification for face recognition"
    - "sparse subspace clustering algorithm theory and applications"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-4 (induced by source paper "a joint convolutional neural networks and context transfer for street scenes labeling"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "anomaly detection in traffic scenes via spatial aware motion reconstruction"
    - "deep image scaling up image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "simultaneous detection and segmentation"
    - "some improvements on deep convolutional neural network based image classification"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "subspace clustering using a symmetric low rank representation"):
  with members:
    - "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"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-6 (induced by source paper "background subtraction via fast robust matrix completion"):
  with members:
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "robust principal component analysis"

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)