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: "real time detection of lane markers in urban streets"
  Abstract: We present a robust and real time approach to lane marker detection in urban streets. It is based on generating a top view of the road, filtering using selective oriented Gaussian filters, using RANSAC line fitting to give initial guesses to a new and fast RANSAC algorithm for fitting Bezier Splines, which is then followed by a post-processing step. Our algorithm can detect all lanes in still images of the street in various conditions, while operating at a rate of 50 Hz and achieving comparable results to previous techniques.

Connected through hyperedge HE-1 (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-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "off road drivable area extraction using 3d lidar data"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "deepdriving learning affordance for direct perception in autonomous driving"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (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-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"
    - "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"

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"
    - "towards end to end lane detection an instance segmentation approach"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "icnet for real time semantic segmentation on high resolution images"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "wavelet convolutional neural networks for texture classification"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "aggregated residual transformations for deep neural 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)