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: "super resolution for overhead imagery using densenets and adversarial learning"
  Abstract: Recent advances in Generative Adversarial Learning allow for new modalities of image super-resolution by learning low to high resolution mappings. In this paper we present our work using Generative Adversarial Networks (GANs) with applications to overhead and satellite imagery. We have experimented with several state-of-the-art architectures. We propose a GAN-based architecture using densely connected convolutional neural networks (DenseNets) to be able to super-resolve overhead imagery with a factor of up to 8x. We have also investigated resolution limits of these networks. We report results on several publicly available datasets, including SpaceNet data and IARPA Multi-View Stereo Challenge, and compare performance with other state-of-the-art architectures.

Connected through hyperedge HE-1 (induced by source paper "super resolving commercial satellite imagery using realistic training data"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "camera lens super resolution"
    - "nima neural image assessment"
    - "learned perceptual image enhancement"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "a comparison of super resolution and nearest neighbors interpolation applied to object detection on satellite data"):
  with members:
    - "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
    - "xview objects in context in overhead imagery"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "cofga classification of fine grained features in aerial images"
    - "deep back projection networks for super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"

Connected through hyperedge HE-3 (induced by source paper "the effects of super resolution on object detection performance in satellite imagery"):
  with members:
    - "satellite imagery multiscale rapid detection with windowed networks"
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "task driven super resolution object detection in low resolution images"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "sensor adaptation for improved semantic segmentation of overhead imagery"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "combogan unrestrained scalability for image domain translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)