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: "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"
  Abstract: Machine learning has proven to be useful in classification and segmentation of images. In this paper, we evaluate a training methodology for pixel-wise segmentation on high resolution satellite images using progressive growing of generative adversarial networks. We apply our model to segmenting building rooftops and compare these results to conventional methods for rooftop segmentation. We present our findings using the SpaceNet version 2 dataset. Progressive GAN training achieved a test accuracy of 93% compared to 89% for traditional GAN training.

Connected through hyperedge HE-1 (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"
    - "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"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-2 (induced by source paper "improving prediction accuracy in building performance models using generative adversarial networks gans"):
  with members:
    - "cactusnets layer applicability as a metric for transfer learning"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generative image modeling using style and structure adversarial networks"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deepsat a learning framework for satellite imagery"
    - "conditional generative adversarial nets"

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)