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: "learning aerial image segmentation from online maps"
  Abstract: This paper deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps that can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled pixel-accurate ground truth of the same city to automatic training data derived from  OpenStreetMap  data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.

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 "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-3 (induced by source paper "improving semantic segmentation of aerial images using patch based attention"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "bam bottleneck attention module"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "feature fusion through multitask cnn for large scale remote sensing image segmentation"
    - "cbam convolutional block attention module"
    - "context encoding for semantic segmentation"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "residual attention network for image classification"

Connected through hyperedge HE-4 (induced by source paper "learning to interpret satellite images in global scale using wikipedia"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images using wikipedia"
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "learning two branch neural networks for image text matching tasks"
    - "snorkel rapid training data creation with weak supervision"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"

Connected through hyperedge HE-5 (induced by source paper "end to end trainable active contours via differentiable rendering"):
  with members:
    - "cnn based semantic segmentation using level set loss"
    - "darnet deep active ray network for building segmentation"
    - "mesh r cnn"
    - "fast interactive object annotation with curve gcn"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "improved breast mass segmentation in mammograms with conditional residual u net"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-6 (induced by source paper "precipitation nowcasting with satellite imagery"):
  with members:
    - "spatiotemporal data fusion for precipitation nowcasting"
    - "road extraction by deep residual u net"
    - "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"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "full resolution residual networks for semantic segmentation in street scenes"
    - "pyramid scene parsing network"

Connected through hyperedge HE-7 (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"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "recurrent neural networks to correct satellite image classification 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-8 (induced by source paper "learning deep structured active contours end to end"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "annotating object instances with a polygon rnn"
    - "semantic instance segmentation via deep metric learning"
    - "deep active contours"
    - "deep watershed transform for instance segmentation"
    - "torontocity seeing the world with a million eyes"
    - "r fcn object detection via region based fully convolutional networks"
    - "instance aware semantic segmentation via multi task network cascades"

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)