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: "spatial as deep spatial cnn for traffic scene understanding"
  Abstract: Convolutional neural networks (CNNs) are usually built by stacking convolutional operations layer-by-layer. Although CNN has shown strong capability to extract semantics from raw pixels, its capacity to capture spatial relationships of pixels across rows and columns of an image is not fully explored. These relationships are important to learn semantic objects with strong shape priors but weak appearance coherences, such as traffic lanes, which are often occluded or not even painted on the road surface as shown in Fig. 1 (a). In this paper, we propose Spatial CNN (SCNN), which generalizes traditional deep layer-by-layer convolutions to slice-byslice convolutions within feature maps, thus enabling message passings between pixels across rows and columns in a layer. Such SCNN is particular suitable for long continuous shape structure or large objects, with strong spatial relationship but less appearance clues, such as traffic lanes, poles, and wall. We apply SCNN on a newly released very challenging traffic lane detection dataset and Cityscapse dataset. The results show that SCNN could learn the spatial relationship for structure output and significantly improves the performance. We show that SCNN outperforms the recurrent neural network (RNN) based ReNet and MRF+CNN (MRFNet) in the lane detection dataset by 8.7% and 4.6% respectively. Moreover, our SCNN won the 1st place on the TuSimple Benchmark Lane Detection Challenge, with an accuracy of 96.53%.

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"
    - "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"
    - "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 "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"
    - "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"

Connected through hyperedge HE-5 (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"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "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 "advances in centerline estimation for autonomous lateral control"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "efficient road lane marking detection with deep learning"
    - "end to end learning for self driving cars"
    - "u net convolutional networks for biomedical image segmentation"
    - "real time detection of lane markers in urban streets"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation 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)