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: "fully convolutional network for automatic road extraction from satellite imagery"
  Abstract: Analysis of high-resolution satellite images has been an important research topic for traffic management, city planning, and road monitoring. One of the problems here is automatic and precise road extraction. From an original image, it is difficult and computationally expensive to extract roads due to presences of other road-like features with straight edges. In this paper, we propose an approach for automatic road extraction based on a fully convolutional neural network of U-net family. This network consists of ResNet-34 pre-trained on ImageNet and decoder adapted from vanilla U-Net. Based on validation results, leaderboard and our own experience this network shows superior results for the DEEPGLOBE - CVPR 2018 road extraction sub-challenge. Moreover, this network uses moderate memory that allows using just one GTX 1080 or 1080ti video cards to perform whole training and makes pretty fast predictions.

Connected through hyperedge HE-1 (induced by source paper "albumentations fast and flexible image augmentations"):
  with members:
    - "camera model identification using convolutional neural networks"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "autoaugment learning augmentation policies from data"
    - "feature pyramid network for multi class land segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "procedural synthesis of remote sensing images for robust change detection with neural networks"):
  with members:
    - "urban change detection for multispectral earth observation using convolutional neural networks"
    - "targeted change detection in remote sensing images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "focal loss for dense object detection"
    - "carla an open urban driving simulator"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "road network reconstruction from satellite images with machine learning supported by topological methods"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "graph reconstruction by discrete morse theory"
    - "lane boundary extraction from satellite imagery"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "streetify using street view imagery and deep learning for urban streets development"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "semantic binary segmentation using convolutional networks without decoders"
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "camera model identification using convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "feature pyramid network for multi class land segmentation"
    - "aligned and non aligned double jpeg detection using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "automatic lesion boundary detection in dermoscopy"):
  with members:
    - "sales forecasting using wavenet within the framework of the kaggle competition"
    - "wide residual networks"
    - "u net convolutional networks for biomedical 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)