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: "end to end lane detection through differentiable least squares fitting"
  Abstract: Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline) is fitted to the post-processed mask next. The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance. In this work, we propose a method to train a lane detector in an end-to-end manner, directly regressing the lane parameters. The architecture consists of two components: a deep network that predicts a segmentation-like weight map for each lane line, and a differentiable least-squares fitting module that returns for each map the parameters of the best-fitting curve in the weighted least-squares sense. These parameters can subsequently be supervised with a loss function of choice. Our method relies on the observation that it is possible to backpropagate through a least-squares fitting procedure. This leads to an end-to-end method where the features are optimized for the true task of interest: the network implicitly learns to generate features that prevent instabilities during the model fitting step, as opposed to two-step pipelines that need to handle outliers with heuristics. Additionally, the system is not just a black box but offers a degree of interpretability because the intermediately generated segmentation-like weight maps can be inspected and visualized. Code and a video is available at github.com/wvangansbeke/LaneDetection_End2End.

Connected through hyperedge HE-1 (induced by source paper "semi local 3d lane detection and uncertainty estimation"):
  with members:
    - "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"
    - "3d lanenet end to end 3d multiple lane detection"

Connected through hyperedge HE-2 (induced by source paper "lane detection in low light conditions using an efficient data enhancement light conditions style transfer"):
  with members:
    - "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"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"

Connected through hyperedge HE-3 (induced by source paper "key points estimation and point instance segmentation approach for lane detection"):
  with members:
    - "robust lane detection from continuous driving scenes using deep neural networks"
    - "centernet keypoint triplets for object detection"
    - "learning lightweight lane detection cnns by self attention distillation"
    - "bottom up object detection by grouping extreme and center points"
    - "towards end to end lane detection an instance segmentation approach"
    - "sgpn similarity group proposal network for 3d point cloud instance segmentation"
    - "learning feature pyramids for human pose estimation"
    - "semantic instance segmentation with a discriminative loss function"

Connected through hyperedge HE-4 (induced by source paper "generating all the roads to rome road layout randomization for improved road marking segmentation"):
  with members:
    - "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"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "cubeslam monocular 3 d object slam"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-6 (induced by source paper "lane detection and classification using cascaded cnns"):
  with members:
    - "deep multi sensor lane detection"
    - "learning to cluster for proposal free instance segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "efficient road lane marking detection with deep learning"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "spatial as deep spatial cnn for traffic scene understanding"

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)