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: "roadtracer automatic extraction of road networks from aerial images"
  Abstract: Mapping road networks is currently both expensive and labor-intensive. High-resolution aerial imagery provides a promising avenue to automatically infer a road network. Prior work uses convolutional neural networks (CNNs) to detect which pixels belong to a road (segmentation), and then uses complex post-processing heuristics to infer graph connectivity. We show that these segmentation methods have high error rates because noisy CNN outputs are difficult to correct. We propose RoadTracer, a new method to automatically construct accurate road network maps from aerial images. RoadTracer uses an iterative search process guided by a CNN-based decision function to derive the road network graph directly from the output of the CNN. We compare our approach with a segmentation method on fifteen cities, and find that at a 5% error rate, RoadTracer correctly captures 45% more junctions across these cities.

Connected through hyperedge HE-1 (induced by source paper "deepdualmapper a gated fusion network for automatic map extraction using aerial images and trajectories"):
  with members:
    - "leveraging crowdsourced gps data for road extraction from aerial imagery"
    - "multi mathbf 3 net segmenting flooded buildings via fusion of multiresolution multisensor and multitemporal satellite imagery"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "label refinement network for coarse to fine semantic segmentation"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

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"
    - "a model predictive motion planner for the iara autonomous car"

Connected through hyperedge HE-3 (induced by source paper "neural turtle graphics for modeling city road layouts"):
  with members:
    - "darnet deep active ray network for building segmentation"
    - "devil is in the edges learning semantic boundaries from noisy annotations"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-4 (induced by source paper "joint segmentation and path classification of curvilinear structures"):
  with members:
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "active learning and proofreading for delineation of curvilinear structures"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "city scale road extraction from satellite imagery v2 road speeds and travel times"):
  with members:
    - "city scale road extraction from satellite imagery"
    - "spacenet a remote sensing dataset and challenge series"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "road extraction by deep residual u net"
    - "focal loss for dense object detection"
    - "beyond the pixel wise loss for topology aware delineation"
    - "torontocity seeing the world with a million eyes"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "leveraging crowdsourced gps data for road extraction from aerial imagery"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "road extraction by deep residual u net"
    - "dota a large scale dataset for object detection in aerial images"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "beyond the pixel wise loss for topology aware delineation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "torontocity seeing the world with a million eyes"

Connected through hyperedge HE-7 (induced by source paper "towards reliable evaluation of road network reconstructions"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "joint segmentation and path classification of curvilinear structures"
    - "spacenet a remote sensing dataset and challenge series"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "beyond the pixel wise loss for topology aware delineation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "roadtagger robust road attribute inference with graph neural networks"):
  with members:
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "assigning a grade accurate measurement of road quality using satellite imagery"
    - "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"

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)