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: "automatic large scale data acquisition via crowdsourcing for crosswalk classification a deep learning approach"
  Abstract: Abstract   Correctly identifying crosswalks is an essential task for the driving activity and mobility autonomy. Many crosswalk classification, detection and localization systems have been proposed in the literature over the years. These systems use different perspectives to tackle the crosswalk classification problem: satellite imagery, cockpit view (from the top of a car or behind the windshield), and pedestrian perspective. Most of the works in the literature are designed and evaluated using small and local datasets, i.e. datasets that present low diversity. Scaling to large datasets imposes a challenge for the annotation procedure. Moreover, there is still need for cross-database experiments in the literature because it is usually hard to collect the data in the same place and conditions of the final application. In this paper, we present a crosswalk classification system based on deep learning. For that, crowdsourcing platforms, such as OpenStreetMap and Google Street View, are exploited to enable automatic training via automatic acquisition and annotation of a large-scale database. Additionally, this work proposes a comparison study of models trained using fully-automatic data acquisition and annotation against models that were partially annotated. Cross-database experiments were also included in the experimentation to show that the proposed methods enable use with real world applications. Our results show that the model trained on the fully-automatic database achieved high overall accuracy (94.12%), and that a statistically significant improvement (to 96.30%) can be achieved by manually annotating a specific part of the database. Finally, the results of the cross-database experiments show that both models are robust to the many variations of image and scenarios, presenting a consistent behavior.

Connected through hyperedge HE-1 (induced by source paper "cross domain car detection using unsupervised image to image translation from day to night"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "on convergence and stability of gans"
    - "pixel and feature level based domain adaption for object detection in autonomous driving"
    - "which training methods for gans do actually converge"
    - "deep learning based large scale automatic satellite crosswalk classification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "self driving cars a survey"):
  with members:
    - "traffic light recognition using deep learning and prior maps for autonomous cars"
    - "effortless deep training for traffic sign detection using templates and arbitrary natural images"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "dct maps compact differentiable lidar maps based on the cosine transform"
    - "visual global localization with a hybrid wnn cnn approach"
    - "map memorization and forgetting in the iara autonomous car"
    - "mapping road lanes using laser remission and deep neural networks"
    - "roadtracer automatic extraction of road networks from aerial images"

Connected through hyperedge HE-3 (induced by source paper "effortless deep training for traffic sign detection using templates and arbitrary natural images"):
  with members:
    - "self driving cars a survey"
    - "self paced deep learning for weakly supervised object detection"
    - "generative one shot learning gol a semi parametric approach to one shot learning in autonomous vision"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "few shot object detection via feature reweighting"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstd a low shot transfer detector for object detection"
    - "deep learning based large scale automatic satellite crosswalk classification"

Connected through hyperedge HE-4 (induced by source paper "traffic light recognition using deep learning and prior maps for autonomous cars"):
  with members:
    - "self driving cars a survey"
    - "a hierarchical deep architecture and mini batch selection method for joint traffic sign and light detection"
    - "yolov3 an incremental improvement"
    - "deep learning based large scale automatic satellite crosswalk classification"
    - "you only look once unified real time object detection"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-5 (induced by source paper "mapping road lanes using laser remission and deep neural networks"):
  with members:
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "a model predictive motion planner for the iara autonomous car"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "vpgnet vanishing point guided network for lane and road marking detection and recognition"
    - "lidar based driving path generation using fully convolutional neural networks"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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)