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: "ternausnetv2 fully convolutional network for instance segmentation"
  Abstract: The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks. In this work we present TernausNetV2 - a simple fully convolutional network that allows extracting objects from a high-resolution satellite imagery on an instance level. The network has popular encoder-decoder type of architecture with skip connections but has a few essential modifications that allows using for semantic as well as for instance segmentation tasks. This approach is universal and allows to extend any network that has been successfully applied for semantic segmentation to perform instance segmentation task. In addition, we generalize network encoder that was pre-trained for RGB images to use additional input channels. It makes possible to use transfer learning from visual to a wider spectral range. For DeepGlobe-CVPR 2018 building detection sub-challenge, based on public leaderboard score, our approach shows superior performance in comparison to other methods. The source code corresponding pre-trained weights are publicly available at this https URL

Connected through hyperedge HE-1 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-2 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dota a large scale dataset for object detection in aerial images"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-3 (induced by source paper "on the evaluation and real world usage scenarios of deep vessel segmentation for retinography"):
  with members:
    - "dunet a deformable network for retinal vessel segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "data efficient image recognition with contrastive predictive coding"
    - "fcos fully convolutional one stage object detection"
    - "mixmatch a holistic approach to semi supervised learning"
    - "deep retinal image understanding"
    - "holistically nested edge detection"

Connected through hyperedge HE-4 (induced by source paper "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"):
  with members:
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "multimodal unsupervised image to image translation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised generalisation with meta auxiliary learning"
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "auxiliary tasks in multi task learning"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "low light image enhancement algorithm based on retinex and generative adversarial network"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "features for multi target multi camera tracking and re identification"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "a twofold siamese network for real time object tracking"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "learning to see in the dark"
    - "msr net low light image enhancement using deep convolutional network"

Connected through hyperedge HE-7 (induced by source paper "a review of object detection models based on convolutional neural network"):
  with members:
    - "advancements in image classification using convolutional neural network"
    - "understanding batch normalization"
    - "focal loss for dense object detection"
    - "single shot refinement neural network for object detection"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "mask r cnn"
    - "hard negative mining for metric learning based zero shot classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "global context for convolutional pose machines"):
  with members:
    - "human pose estimation with spatial contextual information"
    - "real time 2d multi person pose estimation on cpu lightweight openpose"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "fast human pose estimation"
    - "look into person self supervised structure sensitive learning and a new benchmark for human parsing"
    - "learning feature pyramids for human pose estimation"
    - "self adversarial training for human pose estimation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

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)