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: "vpgnet vanishing point guided network for lane and road marking detection and recognition"
  Abstract: In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available.

Connected through hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "deep multi sensor lane detection"
    - "learning lightweight lane detection cnns by self attention distillation"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "evaluating and calibrating uncertainty prediction in regression tasks"
    - "monoloco monocular 3d pedestrian localization and uncertainty estimation"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "towards end to end lane detection an instance segmentation approach"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-3 (induced by source paper "lane detection in low light conditions using an efficient data enhancement light conditions style transfer"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "generative modeling for small data object detection"
    - "learning lightweight lane detection cnns by self attention distillation"
    - "cross domain car detection using unsupervised image to image translation from day to night"
    - "towards end to end lane detection an instance segmentation approach"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "multi lane detection using instance segmentation and attentive voting"):
  with members:
    - "towards end to end lane detection an instance segmentation approach"
    - "restructuring batch normalization to accelerate cnn training"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "an empirical evaluation of deep learning on highway driving"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "real time lane id estimation using recurrent neural networks with dual convention"):
  with members:
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "lanequest an accurate and energy efficient lane detection system"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

Connected through hyperedge HE-7 (induced by source paper "instance wise depth and motion learning from monocular videos"):
  with members:
    - "learning residual flow as dynamic motion from stereo videos"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "learning independent object motion from unlabelled stereoscopic videos"
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "video instance segmentation"
    - "visuomotor understanding for representation learning of driving scenes"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"

Connected through hyperedge HE-8 (induced by source paper "generating all the roads to rome road layout randomization for improved road marking segmentation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "a system for generating complex physically accurate sensor images for automotive applications"
    - "semantic image synthesis with spatially adaptive normalization"
    - "aads augmented autonomous driving simulation using data driven algorithms"
    - "augmented lidar simulator for autonomous driving"
    - "streetgen in base city scale procedural generation of streets road network road surface and street objects"
    - "3d lanenet end to end 3d multiple lane detection"
    - "vadra visual adversarial domain randomization and augmentation"

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)