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: "vanishing point detection with convolutional neural networks"
  Abstract: Inspired by the finding that vanishing point (road tangent) guides driver's gaze, in our previous work we showed that vanishing point attracts gaze during free viewing of natural scenes as well as in visual search (Borji et al., Journal of Vision 2016). We have also introduced improved saliency models using vanishing point detectors (Feng et al., WACV 2016). Here, we aim to predict vanishing points in naturalistic environments by training convolutional neural networks in an end-to-end manner over a large set of road images downloaded from Youtube with vanishing points annotated. Results demonstrate effectiveness of our approach compared to classic approaches of vanishing point detection in the literature.

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"
    - "detecting vanishing points using global image context in a non manhattan world"

Connected through hyperedge HE-2 (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-3 (induced by source paper "digging deeper into egocentric gaze prediction"):
  with members:
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "first person action object detection with egonet"
    - "detecting engagement in egocentric video"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "recurrent mixture density network for spatiotemporal visual attention"
    - "generating videos with scene dynamics"
    - "the evolution of first person vision methods a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "line drawings of natural scenes guide visual attention"):
  with members:
    - "a unified framework for salient structure detection by contour guided visual search"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-5 (induced by source paper "vpgnet vanishing point guided network for lane and road marking detection and recognition"):
  with members:
    - "ssd single shot multibox detector"
    - "an empirical evaluation of deep learning on highway driving"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "pcanet a simple deep learning baseline for image classification"
    - "fully convolutional networks for semantic segmentation"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "deep learning for vanishing point detection using an inverse gnomonic projection"):
  with members:
    - "horizon lines in the wild"
    - "detecting vanishing points using global image context in a non manhattan world"
    - "caffe convolutional architecture for fast feature embedding"

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)