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: "learning lightweight lane detection cnns by self attention distillation"
  Abstract: Training deep models for lane detection is challenging due to the very subtle and sparse supervisory signals inherent in lane annotations. Without learning from much richer context, these models often fail in challenging scenarios, e.g., severe occlusion, ambiguous lanes, and poor lighting conditions. In this paper, we present a novel knowledge distillation approach, i.e., Self Attention Distillation (SAD), which allows a model to learn from itself and gains substantial improvement without any additional supervision or labels. Specifically, we observe that attention maps extracted from a model trained to a reasonable level would encode rich contextual information. The valuable contextual information can be used as a form of 'free' supervision for further representation learning through performing topdown and layer-wise attention distillation within the network itself. SAD can be easily incorporated in any feedforward convolutional neural networks (CNN) and does not increase the inference time. We validate SAD on three popular lane detection benchmarks (TuSimple, CULane and BDD100K) using lightweight models such as ENet, ResNet-18 and ResNet-34. The lightest model, ENet-SAD, performs comparatively or even surpasses existing algorithms. Notably, ENet-SAD has 20 x fewer parameters and runs 10 x faster compared to the state-of-the-art SCNN, while still achieving compelling performance in all benchmarks. Our code is available at this https URL.

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"
    - "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:
    - "end to end lane detection through differentiable least squares fitting"
    - "generative modeling for small data object detection"
    - "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:
    - "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"
    - "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 "real time multi target path prediction and planning for autonomous driving aided by fcn"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "tiev the tongji intelligent electric vehicle in the intelligent vehicle future challenge of china"
    - "dual attention network for scene segmentation"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "sampling based algorithms for optimal motion planning using closed loop prediction"
    - "pyramid scene parsing network"

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)