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: "efficient road lane marking detection with deep learning"
  Abstract: Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS). Limited by the onboard computing power, it is still a challenge to reduce system complexity and maintain high accuracy at the same time. In this paper, we propose a Lane Marking Detector (LMD) using a deep convolutional neural network to extract robust lane marking features. To improve its performance with a target of lower complexity, the dilated convolution is adopted. A shallower and thinner structure is designed to decrease the computational cost. Moreover, we also design post-processing algorithms to construct 3rd-order polynomial models to fit into the curved lanes. Our system shows promising results on the captured road scenes.

Connected through hyperedge HE-1 (induced by source paper "advances in centerline estimation for autonomous lateral control"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "spatial as deep spatial cnn for traffic scene understanding"
    - "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-2 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"
    - "augmented lidar simulator for autonomous driving"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "few shot object detection via feature reweighting"
    - "dynamic graph cnn for learning on point clouds"

Connected through hyperedge HE-3 (induced by source paper "lane detection and classification using cascaded cnns"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "deep multi sensor lane detection"
    - "learning to cluster for proposal free instance segmentation"
    - "towards end to end lane detection an instance segmentation approach"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "multi class lane semantic segmentation using efficient convolutional networks"):
  with members:
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "multi scale context aggregation by dilated convolutions"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "an intelligent safety system for human centered semi autonomous vehicles"):
  with members:
    - "towards end to end lane detection an instance segmentation approach"
    - "human centered autonomous vehicle systems principles of effective shared autonomy"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "imitation learning for vision based lane keeping assistance"
    - "end to end learning for self driving cars"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"

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)