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: "deep learning for vanishing point detection using an inverse gnomonic projection"
  Abstract: We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.

Connected through hyperedge HE-1 (induced by source paper "consac robust multi model fitting by conditional sample consensus"):
  with members:
    - "neural guided ransac learning where to sample model hypotheses"
    - "progressive x efficient anytime multi model fitting algorithm"
    - "learning less is more 6d camera localization via 3d surface regression"
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "multi class model fitting by energy minimization and mode seeking"
    - "learning to find good correspondences"

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"
    - "learning to find good correspondences"
    - "demon depth and motion network for learning monocular stereo"
    - "lift learned invariant feature transform"

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"
    - "deformable convolutional networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"

Connected through hyperedge HE-4 (induced by source paper "learning disentangled representations via independent subspaces"):
  with members:
    - "structuring autoencoders"
    - "repnet weakly supervised training of an adversarial reprojection network for 3d human pose estimation"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "mask guided portrait editing with conditional gans"
    - "uncalibrated non rigid factorisation by independent subspace analysis"
    - "markov chain neural networks"
    - "generating a fusion image one s identity and another s shape"
    - "attgan facial attribute editing by only changing what you want"

Connected through hyperedge HE-5 (induced by source paper "temporally consistent horizon lines"):
  with members:
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "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"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-6 (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"
    - "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"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-7 (induced by source paper "markov chain neural networks"):
  with members:
    - "yolo9000 better faster stronger"
    - "noisy activation functions"
    - "probabilistic neural network training for semi supervised classifiers"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-8 (induced by source paper "object recognition from very few training examples for enhancing bicycle maps"):
  with members:
    - "on support relations and semantic scene graphs"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "random forests versus neural networks what s best for camera localization"
    - "learning to detect and localize many objects from few examples"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

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)