human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "geogan a conditional gan with reconstruction and style loss to generate standard layer of maps from satellite images"):
  Abstract: Automatically generating maps from satellite images is an important task. There is a body of literature which tries to address this challenge. We created a more expansive survey of the task by experimenting with different models and adding new loss functions to improve results. We created a database of pairs of satellite images and the corresponding map of the area. Our model translates the satellite image to the corresponding standard layer map image using three main model architectures: (i) a conditional Generative Adversarial Network (GAN) which compresses the images down to a learned embedding, (ii) a generator which is trained as a normalizing flow (RealNVP) model, and (iii) a conditional GAN where the generator translates via a series of convolutions to the standard layer of a map and the discriminator input is the concatenation of the real/generated map and the satellite image. Model (iii) was by far the most promising of three models. To improve the results we also added a reconstruction loss and style transfer loss in addition to the GAN losses. The third model architecture produced the best quality of sampled images. In contrast to the other generative model where evaluation of the model is a challenging problem. since we have access to the real map for a given satellite image, we are able to assign a quantitative metric to the quality of the generated images in addition to inspecting them visually. While we are continuing to work on increasing the accuracy of the model, one challenge has been the coarse resolution of the data which upper-bounds the quality of the results of our model. Nevertheless, as will be seen in the results, the generated map is more accurate in the features it produces since the generator architecture demands a pixel-wise image translation/pixel-wise coloring. A video presentation summarizing this paper is available at: this https URL

Includes members:
  Member "predicting food security outcomes using convolutional neural networks cnns for satellite tasking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty")
  Member "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dc al gan pseudoprogression and true tumor progression of glioblastoma multiform image classification based on dcgan and alexnet")
  Member "predicting us state level agricultural sentiment as a measure of food security with tweets from farming communities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi supervised multitask learning on multispectral satellite images using wasserstein generative adversarial networks gans for predicting poverty")
  Member "deepglobe 2018 a challenge to parse the earth through satellite images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "what is it like down there generating dense ground level views and image features from overhead imagery using conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bridging the domain gap for ground to aerial image matching")
  Member "everybody dance now":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

Question: Please tell me which class the source paper 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)