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: "robust lane detection from continuous driving scenes using deep neural networks"
  Abstract: Lane detection in driving scenes is an important module for autonomous vehicles and advanced driver assistance systems. In recent years, many sophisticated lane detection methods have been proposed. However, most methods focus on detecting the lane from one single image, and often lead to unsatisfactory performance in handling some extremely-bad situations such as heavy shadow, severe mark degradation, serious vehicle occlusion, and so on. In fact, lanes are continuous line structures on the road. Consequently, the lane that cannot be accurately detected in one current frame may potentially be inferred out by incorporating information of previous frames. To this end, we investigate lane detection by using multiple frames of a continuous driving scene, and propose a hybrid deep architecture by combining the convolutional neural network (CNN) and the recurrent neural network (RNN). Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction. Extensive experiments on two large-scale datasets demonstrate that, the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "fusionlane multi sensor fusion for lane marking semantic segmentation using deep neural networks"):
  with members:
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning deconvolution network for semantic segmentation"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "fastdraw addressing the long tail of lane detection by adapting a sequential prediction network"):
  with members:
    - "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"
    - "towards end to end lane detection an instance segmentation approach"
    - "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"

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)