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: "road segmentation in sar satellite images with deep fully convolutional neural networks"
  Abstract: Remote sensing is extensively used in cartography. As transportation networks grow and change, extracting roads automatically from satellite images is crucial to keep maps up-to-date. Synthetic aperture radar (SAR) satellites can provide high-resolution topographical maps. However, roads are difficult to identify in these data as they look visually similar to targets, such as rivers and railways. Most road extraction methods on SAR images still rely on a prior segmentation performed by the classical computer vision algorithms. Few works study the potential of deep learning techniques, despite their successful applications to optical imagery. This letter presents an evaluation of fully convolutional neural networks (FCNNs) for road segmentation in SAR images. We study the relative performance of early and state-of-the-art networks after carefully enhancing their sensitivity toward thin objects by adding the spatial tolerance rules. Our models show promising results, successfully extracting most of the roads in our test data set. This shows that although FCNNs natively lack efficiency for road segmentation, they are capable of good results if properly tuned. As the segmentation quality does not scale well with the increasing depth of the networks, the design of specialized architectures for roads extraction should yield better performances.

Connected through hyperedge HE-1 (induced by source paper "image conditioned graph generation for road network extraction"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "path augmented graph transformer network"
    - "roberta a robustly optimized bert pretraining approach"
    - "efficient graph generation with graph recurrent attention networks"
    - "spacenet a remote sensing dataset and challenge series"
    - "neural particle smoothing for sampling from conditional sequence models"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "nl linknet toward lighter but more accurate road extraction with non local operations"):
  with members:
    - "residual non local attention networks for image restoration"
    - "attentional road safety networks"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "non local recurrent network for image restoration"
    - "road extraction by deep residual u net"
    - "non local neural networks"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "hr sar net a deep neural network for urban scene segmentation from high resolution sar data"):
  with members:
    - "the sen1 2 dataset for deep learning in sar optical data fusion"
    - "w net a deep model for fully unsupervised image segmentation"
    - "deep structured features for semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "fully convolutional networks and generative adversarial networks applied to sclera segmentation"):
  with members:
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "segdensenet iris segmentation for pre and post cataract surgery"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "semantic segmentation using adversarial networks"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "robust iris segmentation based on fully convolutional networks and generative adversarial networks"):
  with members:
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "fully convolutional networks and generative adversarial networks applied to sclera segmentation"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "a robust real time automatic license plate recognition based on the yolo detector"
    - "multinet real time joint semantic reasoning for autonomous driving"
    - "semantic segmentation using adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "you only look once unified real time object detection"

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)