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 extraction by deep residual u net"
  Abstract: Road extraction from aerial images has been a hot research topic in the field of remote sensing image analysis. In this letter, a semantic segmentation neural network, which combines the strengths of residual learning and U-Net, is proposed for road area extraction. The network is built with residual units and has similar architecture to that of U-Net. The benefits of this model are twofold: first, residual units ease training of deep networks. Second, the rich skip connections within the network could facilitate information propagation, allowing us to design networks with fewer parameters, however, better performance. We test our network on a public road data set and compare it with U-Net and other two state-of-the-art deep-learning-based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.

Connected through hyperedge HE-1 (induced by source paper "no surprises training robust lung nodule detection for low dose ct scans by augmenting with adversarial attacks"):
  with members:
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "towards neural networks that provably know when they don t know"
    - "correlation via synthesis end to end nodule image generation and radiogenomic map learning based on generative adversarial network"
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "generative adversarial network in medical imaging a review"
    - "batch normalization is a cause of adversarial vulnerability"
    - "synthesizing diverse lung nodules wherever massively 3d multi conditional gan based ct image augmentation for object detection"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"

Connected through hyperedge HE-2 (induced by source paper "from w net to cdgan bitemporal change detection via deep learning techniques"):
  with members:
    - "getnet a general end to end 2 d cnn framework for hyperspectral image change detection"
    - "psgan a generative adversarial network for remote sensing image pan sharpening"
    - "remote sensing image fusion based on two stream fusion network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures"):
  with members:
    - "a novel topology optimization approach using conditional deep learning"
    - "deep learning for determining a near optimal topological design without any iteration"
    - "a deep convolutional neural network for topology optimization with strong generalization ability"
    - "prediction and optimization of mechanical properties of composites using convolutional neural networks"
    - "3d topology optimization using convolutional neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep learning accelerated topology optimization of linear and nonlinear structures"):
  with members:
    - "a novel topology optimization approach using conditional deep learning"
    - "deep learning for determining a near optimal topological design without any iteration"
    - "a deep convolutional neural network for topology optimization with strong generalization ability"
    - "prediction and optimization of mechanical properties of composites using convolutional neural networks"
    - "3d topology optimization using convolutional neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "resunet a a deep learning framework for semantic segmentation of remotely sensed data"):
  with members:
    - "rethinking imagenet pre training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "context encoding for semantic segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"

Connected through hyperedge HE-6 (induced by source paper "satellite imagery multiscale rapid detection with windowed networks"):
  with members:
    - "xview objects in context in overhead imagery"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "a survey on object detection in optical remote sensing images"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-7 (induced by source paper "software engineering practice in the development of deep learning applications"):
  with members:
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "machine learning testing survey landscapes and horizons"
    - "requirements engineering challenges in building ai based complex systems"
    - "a comprehensive study on deep learning bug characteristics"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"
    - "dlfuzz differential fuzzing testing of deep learning systems"

Connected through hyperedge HE-8 (induced by source paper "precipitation nowcasting with satellite imagery"):
  with members:
    - "spatiotemporal data fusion for precipitation nowcasting"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "learning aerial image segmentation from online maps"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "pyramid scene parsing 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)