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: "multi task learning for segmentation of building footprints with deep neural networks"
  Abstract: The increased availability of high resolution satellite imagery allows to sense very detailed structures on the surface of our planet. Access to such information opens up new directions in the analysis of remote sensing imagery. However, at the same time this raises a set of new challenges for existing pixel-based prediction methods, such as semantic segmentation approaches. While deep neural networks have achieved significant advances in the semantic segmentation of high resolution images in the past, most of the existing approaches tend to produce predictions with poor boundaries. In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss. We evaluate our approach on Inria Aerial Image Labeling Dataset which contains large-scale and high resolution images. Our results show that we are able to outperform state-of-the-art methods by 8.3\% without any additional post-processing step.

Connected through hyperedge HE-1 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf"):
  with members:
    - "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"
    - "learning spectral spatial temporal features via a recurrent convolutional neural network for change detection in multispectral imagery"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "pixel adaptive convolutional neural networks"
    - "relation network for multi label aerial image classification"
    - "building footprint generation using improved generative adversarial networks"
    - "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"

Connected through hyperedge HE-2 (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"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"

Connected through hyperedge HE-3 (induced by source paper "distance transform regression for spatially aware deep semantic segmentation"):
  with members:
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "casenet deep category aware semantic edge detection"
    - "richer convolutional features for edge detection"
    - "convolutional oriented boundaries from image segmentation to high level tasks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "a multi task u net for segmentation with lazy labels"):
  with members:
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "coarse to fine semantic segmentation from image level labels"
    - "a two stage 3d unet framework for multi class segmentation on full resolution image"
    - "deep learning with mixed supervision for brain tumor segmentation"
    - "coarse to fine volumetric segmentation of teeth in cone beam ct"
    - "multiclass weighted loss for instance segmentation of cluttered cells"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "a distance map regularized cnn for cardiac cine mr image segmentation"):
  with members:
    - "deep visual domain adaptation a survey"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "left ventricle segmentation and quantification from cardiac cine mr images via multi task learning"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "automatic cardiac disease assessment on cine mri via time series segmentation and domain specific features"
    - "a survey on deep learning in medical image analysis"
    - "an overview of multi task learning in deep neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "a comparison and strategy of semantic segmentation on remote sensing images"):
  with members:
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-7 (induced by source paper "yuvmultinet real time yuv multi task cnn for autonomous driving"):
  with members:
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "yolov3 an incremental improvement"
    - "cross connected networks for multi task learning of detection and segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "ssd single shot multibox detector"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "solarnet a deep learning framework to map solar power plants in china from satellite imagery"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "non local neural networks"
    - "identity mappings in deep residual networks"
    - "r fcn object detection via region based fully convolutional networks"
    - "learning deep features for discriminative localization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fully convolutional networks for 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)