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 vanishing points using global image context in a non manhattan world"
  Abstract: We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains. A key element of our approach is the use of global image context, extracted with a deep convolutional network, to constrain the set of candidates under consideration. Our method does not make a Manhattan-world assumption and can operate effectively on scenes with only a single horizontal vanishing point. We evaluate our approach on three benchmark datasets and achieve state-of-the-art performance on each. In addition, our approach is significantly faster than the previous best method.

Connected through hyperedge HE-1 (induced by source paper "vanishing point detection with direct and transposed fast hough transform inside the neural network"):
  with members:
    - "computational optimization of convolutional neural networks using separated filters architecture"
    - "vanishing point detection with convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "neural guided ransac learning where to sample model hypotheses"):
  with members:
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "on the fly adaptation of regression forests for online camera relocalisation"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "learning to find good correspondences"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-3 (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-4 (induced by source paper "temporally consistent horizon lines"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "a perceptual measure for deep single image camera calibration"
    - "improved regularization of convolutional neural networks with cutout"
    - "sgdr stochastic gradient descent with restarts"
    - "densely connected convolutional networks"
    - "horizon lines in the wild"
    - "temporally consistent motion segmentation from rgb d video"

Connected through hyperedge HE-5 (induced by source paper "farsa fully automated roadway safety assessment"):
  with members:
    - "a unified model for near and remote sensing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "understanding and mapping natural beauty"
    - "deep learning the city quantifying urban perception at a global scale"
    - "torontocity seeing the world with a million eyes"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-6 (induced by source paper "blind geometric distortion correction on images through deep learning"):
  with members:
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "horizon lines in the wild"
    - "u net convolutional networks for biomedical image segmentation"
    - "dense optical flow prediction from a static image"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "houghnet neural network architecture for vanishing points detection"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "deep learning for vanishing point detection using an inverse gnomonic projection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-8 (induced by source paper "4 d scene alignment in surveillance video"):
  with members:
    - "a geometric approach to obtain a bird s eye view from an image"
    - "horizon lines in the wild"

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)