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: "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"
  Abstract: Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.

Connected through hyperedge HE-1 (induced by source paper "online structured sparsity based moving object detection from satellite videos"):
  with members:
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "dota a large scale dataset for object detection in aerial images"
    - "tracking in aerial hyperspectral videos using deep kernelized correlation filters"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "corola a sequential solution to moving object detection using low rank approximation"

Connected through hyperedge HE-2 (induced by source paper "jumping manifolds geometry aware dense non rigid structure from motion"):
  with members:
    - "a simple prior free method for non rigid structure from motion factorization revisited"
    - "non rigid structure from motion prior free factorization method revisited"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "scalable dense monocular surface reconstruction"
    - "dense non rigid structure from motion made easy a spatial temporal smoothness based solution"

Connected through hyperedge HE-3 (induced by source paper "multiple manifolds metric learning with application to image set classification"):
  with members:
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "building deep networks on grassmann manifolds"
    - "a riemannian network for spd matrix learning"

Connected through hyperedge HE-4 (induced by source paper "alignment distances on systems of bags"):
  with members:
    - "dynamic texture and scene classification by transferring deep image features"

Connected through hyperedge HE-5 (induced by source paper "disturbance grassmann kernels for subspace based learning"):
  with members:
    - "dropout training for support vector machines"

Connected through hyperedge HE-6 (induced by source paper "tensor train recurrent neural networks for video classification"):
  with members:
    - "ultimate tensorization compressing convolutional and fc layers alike"
    - "spatiotemporal residual networks for video action recognition"
    - "tensorizing neural networks"
    - "learning both weights and connections for efficient neural networks"
    - "unsupervised learning of video representations using lstms"
    - "beyond short snippets deep networks for video classification"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "action recognition using visual attention"

Connected through hyperedge HE-7 (induced by source paper "a spatiotemporal oriented energy network for dynamic texture recognition"):
  with members:
    - "structured receptive fields in cnns"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "pcanet a simple deep learning baseline for image classification"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "network in network"
    - "invariant scattering convolution networks"

Connected through hyperedge HE-8 (induced by source paper "localized lrr on grassmann manifolds an extrinsic view"):
  with members:
    - "low rank representation over the manifold of curves"
    - "expanding the family of grassmannian kernels an embedding perspective"
    - "from manifold to manifold geometry aware dimensionality reduction for spd matrices"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "compressive acquisition of dynamic scenes"
    - "principal manifolds and nonlinear dimension reduction via local tangent space alignment"

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)