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: "horizon lines in the wild"
  Abstract: The horizon line is an important contextual attribute for a wide variety of image understanding tasks. As such, many methods have been proposed to estimate its location from a single image. These methods typically require the image to contain specific cues, such as vanishing points, coplanar circles, and regular textures, thus limiting their real-world applicability. We introduce a large, realistic evaluation dataset, Horizon Lines in the Wild (HLW), containing natural images with labeled horizon lines. Using this dataset, we investigate the application of convolutional neural networks for directly estimating the horizon line, without requiring any explicit geometric constraints or other special cues. An extensive evaluation shows that using our CNNs, either in isolation or in conjunction with a previous geometric approach, we achieve state-of-the-art results on the challenging HLW dataset and two existing benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "detecting lane and road markings at a distance with perspective transformer layers"):
  with members:
    - "learning to cluster for proposal free instance segmentation"
    - "the right angled perspective improving the understanding of road scenes using boosted inverse perspective mapping"
    - "towards end to end lane detection an instance segmentation approach"
    - "vh hfcn based parking slot and lane markings segmentation on panoramic surround view"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "semantic instance segmentation with a discriminative loss function"
    - "spatial as deep spatial cnn for traffic scene understanding"

Connected through hyperedge HE-2 (induced by source paper "uprightnet geometry aware camera orientation estimation from single images"):
  with members:
    - "learning the depths of moving people by watching frozen people"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "groundnet segmentation aware monocular ground plane estimation with geometric consistency"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "deep depth completion of a single rgb d image"

Connected through hyperedge HE-3 (induced by source paper "car pose in context accurate pose estimation with ground plane constraints"):
  with members:
    - "a geometric approach to obtain a bird s eye view from an image"
    - "resolving 3d human pose ambiguities with 3d scene constraints"
    - "meta sim learning to generate synthetic datasets"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "a proposal based solution to spatio temporal action detection in untrimmed videos"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"
    - "groundnet monocular ground plane normal estimation with geometric consistency"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "clear skies ahead towards real time automatic sky replacement in video"):
  with members:
    - "icnet for real time semantic segmentation on high resolution images"
    - "segmenting sky pixels in images"
    - "sfm net learning of structure and motion from video"
    - "deep image harmonization"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "identity mappings in deep residual networks"
    - "direct sparse odometry"
    - "learning to refine object segments"

Connected through hyperedge HE-7 (induced by source paper "learning perspective undistortion of portraits"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "what does 2d geometric information really tell us about 3d face shape"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "a perceptual measure for deep single image camera calibration"
    - "synthesizing normalized faces from facial identity features"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "towards large pose face frontalization in the wild"

Connected through hyperedge HE-8 (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"
    - "temporally consistent motion segmentation from rgb d video"
    - "wavenet a generative model for raw audio"

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)