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: "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
  Abstract: In this work, we investigate the use of OpenStreetMap data for semantic labeling of Earth Observation images. Deep neural networks have been used in the past for remote sensing data classification from various sensors, including multispectral, hyperspectral, SAR and LiDAR data. While OpenStreetMap has already been used as ground truth data for training such networks, this abundant data source remains rarely exploited as an input information layer. In this paper, we study different use cases and deep network architectures to leverage OpenStreetMap data for semantic labeling of aerial and satellite images. Especially , we look into fusion based architectures and coarse-to-fine segmentation to include the OpenStreetMap layer into multispectral-based deep fully convolutional networks. We illustrate how these methods can be successfully used on two public datasets: ISPRS Potsdam and DFC2017. We show that OpenStreetMap data can efficiently be integrated into the vision-based deep learning models and that it significantly improves both the accuracy performance and the convergence speed of the networks.

Connected through hyperedge HE-1 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "deepdualmapper a gated fusion network for automatic map extraction using aerial images and trajectories"):
  with members:
    - "leveraging crowdsourced gps data for road extraction from aerial imagery"
    - "multi mathbf 3 net segmenting flooded buildings via fusion of multiresolution multisensor and multitemporal satellite imagery"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "label refinement network for coarse to fine semantic segmentation"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "dense dilated convolutions merging network for semantic mapping of remote sensing images"):
  with members:
    - "on the convergence of adam and beyond"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "understanding convolution for semantic segmentation"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised semantic segmentation of satellite images"):
  with members:
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "correcting rural building annotations in openstreetmap using convolutional neural networks"):
  with members:
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "recurrent neural networks to correct satellite image classification maps"
    - "learning aerial image segmentation from online maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-6 (induced by source paper "arcticnet a deep learning solution to classify arctic wetlands"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "learning to interpret satellite images using wikipedia"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "snorkel rapid training data creation with weak supervision"
    - "functional map of the world"
    - "yolo9000 better faster stronger"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "deep learning approach for building detection in satellite multispectral imagery"):
  with members:
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "binary distance transform to improve feature extraction"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"
    - "rich feature hierarchies for accurate object detection and semantic 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)