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: "deepsat a learning framework for satellite imagery"
  Abstract: Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. The contributions of this paper are twofold - (1) first, we present two new satellite datasets called SAT-4 and SAT-6, and (2) then, we propose a classification framework that extracts features from an input image, normalizes them and feeds the normalized feature vectors to a Deep Belief Network for classification. On the SAT-4 dataset, our best network produces a classification accuracy of 97.95% and outperforms three state-of-the-art object recognition algorithms, namely - Deep Belief Networks, Convolutional Neural Networks and Stacked Denoising Autoencoders by ~11%. On SAT-6, it produces a classification accuracy of 93.9% and outperforms the other algorithms by ~15%. Comparative studies with a Random Forest classifier show the advantage of an unsupervised learning approach over traditional supervised learning techniques. A statistical analysis based on Distribution Separability Criterion and Intrinsic Dimensionality Estimation substantiates the effectiveness of our approach in learning better representations for satellite imagery.

Connected through hyperedge HE-1 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-2 (induced by source paper "context aware design of cyber physical human systems cphs"):
  with members:
    - "improving prediction accuracy in building performance models using generative adversarial networks gans"
    - "improving route choice models by incorporating contextual factors via knowledge distillation"
    - "why do you take that route"
    - "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"
    - "unsupervised learning using pretrained cnn and associative memory bank"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "temporal interpolation of geostationary satellite imagery with task specific optical flow"):
  with members:
    - "depth aware video frame interpolation"
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "land use classification in remote sensing images by convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "deepsat v2 feature augmented convolutional neural nets for satellite image classification"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"):
  with members:
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "adversarial deep structural networks for mammographic mass segmentation"

Connected through hyperedge HE-6 (induced by source paper "pcgan char progressively trained classifier generative adversarial networks for classification of noisy handwritten bangla characters"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "pixel level reconstruction and classification for noisy handwritten bangla characters"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "context encoders feature learning by inpainting"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "optical flow for intermediate frame interpolation of multispectral geostationary satellite data"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "land use classification in remote sensing images by convolutional neural networks"

Connected through hyperedge HE-8 (induced by source paper "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"):
  with members:
    - "cloud net an end to end cloud detection algorithm for landsat 8 imagery"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "the sen1 2 dataset for deep learning in sar optical data fusion"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "dota a large scale dataset for object detection in aerial images"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deep residual learning for image recognition"

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)