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: "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
  Abstract: The knowledge about the placement and appearance of lane markings is a prerequisite for the creation of maps with high precision, necessary for autonomous driving, infrastructure monitoring, lanewise traffic management, and urban planning. Lane markings are one of the important components of such maps. Lane markings convey the rules of roads to drivers. While these rules are learned by humans, an autonomous driving vehicle should be taught to learn them to localize itself. Therefore, accurate and reliable lane-marking semantic segmentation in the imagery of roads and highways is needed to achieve such goals. We use airborne imagery that can capture a large area in a short period of time by introducing an aerial lane marking data set. In this paper, we propose a symmetric fully convolutional neural network enhanced by wavelet transform in order to automatically carry out lane-marking segmentation in aerial imagery. Due to a heavily unbalanced problem in terms of a number of lane-marking pixels compared with background pixels, we use a customized loss function as well as a new type of data augmentation step. We achieve a high accuracy in pixelwise localization of lane markings compared with the state-of-the-art methods without using the third-party information. In this paper, we introduce the first high-quality data set used within our experiments, which contains a broad range of situations and classes of lane markings representative of today’s transportation systems. This data set will be publicly available, and hence, it can be used as the benchmark data set for future algorithms within this domain.

Connected through hyperedge HE-1 (induced by source paper "fusionlane multi sensor fusion for lane marking semantic segmentation using deep neural networks"):
  with members:
    - "robust lane detection from continuous driving scenes using deep 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-2 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "convolutional crfs for semantic segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"

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:
    - "robust lane detection from continuous driving scenes using deep 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"

Connected through hyperedge HE-4 (induced by source paper "ai based pilgrim detection using convolutional neural networks"):
  with members:
    - "activity monitoring of islamic prayer salat postures using deep learning"
    - "aerial images processing for car detection using convolutional neural networks comparison between faster r cnn and yolov3"
    - "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "yolov3 an incremental improvement"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "car detection using unmanned aerial vehicles comparison between faster r cnn and yolov3"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "roadtagger robust road attribute inference with graph neural networks"):
  with members:
    - "assigning a grade accurate measurement of road quality using satellite imagery"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "gated graph sequence neural networks"
    - "tensorflow a system for large scale machine learning"
    - "gated feedback recurrent neural networks"
    - "a comparison and evaluation of map construction algorithms using vehicle tracking data"

Connected through hyperedge HE-6 (induced by source paper "aerial images processing for car detection using convolutional neural networks comparison between faster r cnn and yolov3"):
  with members:
    - "yolov3 an incremental improvement"
    - "car detection using unmanned aerial vehicles comparison between faster r cnn and yolov3"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "shuffledet real time vehicle detection network in on board embedded uav imagery"):
  with members:
    - "advanced steel microstructural classification by deep learning methods"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "pelee a real time object detection system on mobile devices"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deformable convolutional networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "drone based object counting by spatially regularized regional proposal 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)