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: "detecting dominant vanishing points in natural scenes with application to composition sensitive image retrieval"
  Abstract: Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.

Connected through hyperedge HE-1 (induced by source paper "neurvps neural vanishing point scanning via conic convolution"):
  with members:
    - "spherical cnns on unstructured grids"
    - "blurring the line between structure and learning to optimize and adapt receptive fields"
    - "learning to reconstruct 3d manhattan wireframes from a single image"
    - "end to end wireframe parsing"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "active convolution learning the shape of convolution for image classification"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "deformable convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "enhanced signal recovery via sparsity inducing image priors"):
  with members:
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "deep convolutional neural network for inverse problems in imaging"
    - "accelerating the super resolution convolutional neural network"
    - "screen content image segmentation using sparse smooth decomposition"
    - "accurate image super resolution using very deep convolutional networks"
    - "self tuned deep super resolution"
    - "dfdl discriminative feature oriented dictionary learning for histopathological image classification"
    - "deep residual learning for image recognition"

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)