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: "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
  Abstract: This paper describes our approach to the DSTL Satellite Imagery Feature Detection challenge run by Kaggle. The primary goal of this challenge is accurate semantic segmentation of different classes in satellite imagery. Our approach is based on an adaptation of fully convolutional neural network for multispectral data processing. In addition, we defined several modifications to the training objective and overall training pipeline, e.g. boundary effect estimation, also we discuss usage of data augmentation strategies and reflectance indices. Our solution scored third place out of 419 entries. Its accuracy is comparable to the first two places, but unlike those solutions, it doesn't rely on complex ensembling techniques and thus can be easily scaled for deployment in production as a part of automatic feature labeling systems for satellite imagery analysis.

Connected through hyperedge HE-1 (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"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "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"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-2 (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"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "longevity associated geometry identified in satellite images sidewalks driveways and hiking trails"):
  with members:
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"
    - "a unified approach to interpreting model predictions"
    - "a deep learning approach for population estimation from satellite imagery"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi task learning for segmentation of building footprints with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "meta learning initializations for image segmentation"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "fss 1000 a 1000 class dataset for few shot segmentation"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "canet class agnostic segmentation networks with iterative refinement and attentive few shot learning"
    - "on first order meta learning algorithms"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-5 (induced by source paper "segmenting ships in satellite imagery with squeeze and excitation u net"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "deep landscape features for improving vector borne disease prediction"):
  with members:
    - "use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-7 (induced by source paper "dense labeling of large remote sensing imagery with convolutional neural networks a simple and faster alternative to stitching output label maps"):
  with members:
    - "application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "automatic detection of solar photovoltaic arrays in high resolution aerial imagery"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-8 (induced by source paper "tiling and stitching segmentation output for remote sensing basic challenges and recommendations"):
  with members:
    - "application of a semantic segmentation convolutional neural network for accurate automatic detection and mapping of solar photovoltaic arrays in aerial imagery"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "automatic detection of solar photovoltaic arrays in high resolution aerial imagery"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"

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)