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: "the effects of super resolution on object detection performance in satellite imagery"
  Abstract: We explore the application of super-resolution techniques to satellite imagery, and the effects of these techniques on object detection algorithm performance. Specifically, we enhance satellite imagery beyond its native resolution, and test if we can identify various types of vehicles, planes, and boats with greater accuracy than native resolution. Using the Very Deep Super-Resolution (VDSR) framework and a custom Random Forest Super-Resolution (RFSR) framework we generate enhancement levels of 2x, 4x, and 8x over five distinct resolutions ranging from 30 cm to 4.8 meters. Using both native and super-resolved data, we then train several custom detection models using the SIMRDWN object detection framework. SIMRDWN combines a number of popular object detection algorithms (e.g. SSD, YOLO) into a unified framework that is designed to rapidly detect objects in large satellite images. This approach allows us to quantify the effects of super-resolution techniques on object detection performance across multiple classes and resolutions. We also quantify the performance of object detection as a function of native resolution and object pixel size. For our test set we note that performance degrades from mean average precision (mAP) = 0.53 at 30 cm resolution, down to mAP = 0.11 at 4.8 m resolution. Super-resolving native 30 cm imagery to 15 cm yields the greatest benefit; a 13-36% improvement in mAP. Super-resolution is less beneficial at coarser resolutions, though still provides a small improvement in performance.

Connected through hyperedge HE-1 (induced by source paper "longevity associated geometry identified in satellite images sidewalks driveways and hiking trails"):
  with members:
    - "use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"
    - "a unified approach to interpreting model predictions"
    - "a deep learning approach for population estimation from satellite imagery"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "multi task learning for segmentation of building footprints with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "an end to end framework for low resolution remote sensing semantic segmentation"):
  with members:
    - "dynamic multicontext segmentation of remote sensing images based on convolutional networks"
    - "task driven super resolution object detection in low resolution images"
    - "ssd single shot multibox detector"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "two stream action recognition oriented video super resolution"):
  with members:
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"
    - "memnet a persistent memory network for image restoration"

Connected through hyperedge HE-4 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "xview objects in context in overhead imagery"
    - "spatially transformed adversarial examples"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-5 (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"
    - "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"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-6 (induced by source paper "training set effect on super resolution for automated target recognition"):
  with members:
    - "on training deep networks for satellite image super resolution"
    - "deep learning for single image super resolution a brief review"
    - "srgan training dataset matters"
    - "a deep journey into super resolution a survey"
    - "yolov3 an incremental improvement"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "mask r cnn"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "on the classification distortion perception tradeoff"):
  with members:
    - "bridging the gap between computational photography and visual recognition"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "the perception distortion tradeoff"
    - "generative image inpainting with contextual attention"
    - "deep bilateral learning for real time image enhancement"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "perceptual losses for real time style transfer and super resolution"
    - "compression artifacts reduction by a deep convolutional network"

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)