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: "towards end to end lane detection an instance segmentation approach"
  Abstract: Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a combination of highly-specialized, hand-crafted features and heuristics, usually followed by post-processing techniques, that are computationally expensive and prone to scalability due to road scene variations. More recent approaches leverage deep learning models, trained for pixel-wise lane segmentation, even when no markings are present in the image due to their big receptive field. Despite their advantages, these methods are limited to detecting a pre-defined, fixed number of lanes, e.g. ego-lanes, and can not cope with lane changes. In this paper, we go beyond the aforementioned limitations and propose to cast the lane detection problem as an instance segmentation problem - in which each lane forms its own instance - that can be trained end-to-end. To parametrize the segmented lane instances before fitting the lane, we further propose to apply a learned perspective transformation, conditioned on the image, in contrast to a fixed "bird's-eye view" transformation. By doing so, we ensure a lane fitting which is robust against road plane changes, unlike existing approaches that rely on a fixed, pre-defined transformation. In summary, we propose a fast lane detection algorithm, running at 50 fps, which can handle a variable number of lanes and cope with lane changes. We verify our method on the tuSimple dataset and achieve competitive results.

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"
    - "3d lanenet end to end 3d multiple lane detection"

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"
    - "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"
    - "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"
    - "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-4 (induced by source paper "key points estimation and point instance segmentation approach for lane detection"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "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"
    - "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-5 (induced by source paper "multi lane detection using instance segmentation and attentive voting"):
  with members:
    - "restructuring batch normalization to accelerate cnn training"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "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-6 (induced by source paper "fastdraw addressing the long tail of lane detection by adapting a sequential prediction network"):
  with members:
    - "robust lane detection from continuous driving scenes using deep neural networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "squeezesegv2 improved model structure and unsupervised domain adaptation for road object segmentation from a lidar point cloud"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "domain stylization a strong simple baseline for synthetic to real image domain adaptation"
    - "learning to drive in a day"

Connected through hyperedge HE-7 (induced by source paper "robust lane detection from continuous driving scenes using deep neural networks"):
  with members:
    - "a deep spatio temporal fuzzy neural network for passenger demand prediction"
    - "rethinking imagenet pre training"
    - "privacy preserving collaborative deep learning with unreliable participants"
    - "improved deep hashing with soft pairwise similarity for multi label image retrieval"
    - "youtube vos sequence to sequence video object segmentation"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "decomposing motion and content for natural video sequence prediction"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"

Connected through hyperedge HE-8 (induced by source paper "learning lightweight lane detection cnns by self attention distillation"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "learning to steer by mimicking features from heterogeneous auxiliary networks"
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "residual attention network for image classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

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)