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: "recurrent neural networks to correct satellite image classification maps"
  Abstract: While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose, and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments, we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.

Connected through hyperedge HE-1 (induced by source paper "unsupervised and unregistered hyperspectral image super resolution with mutual dirichlet net"):
  with members:
    - "unsupervised sparse dirichlet net for hyperspectral image super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast and accurate single image super resolution via information distillation network"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"
    - "mine mutual information neural estimation"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-2 (induced by source paper "correcting rural building annotations in openstreetmap using convolutional neural networks"):
  with members:
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "learning aerial image segmentation from online maps"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-3 (induced by source paper "unsupervised sparse dirichlet net for hyperspectral image super resolution"):
  with members:
    - "sparse signal recovery via generalized entropy functions minimization"
    - "endnet sparse autoencoder network for endmember extraction and hyperspectral unmixing"
    - "boosting the accuracy of multispectral image pansharpening by learning a deep residual network"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "hyperspectral pansharpening a review"

Connected through hyperedge HE-4 (induced by source paper "cnns fusion for building detection in aerial images for the building detection challenge"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ensembles of multiple models and architectures for robust brain tumour segmentation"
    - "deep cnn ensembles and suggestive annotations for infant brain mri segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep watershed transform for instance segmentation"
    - "deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label free angiography"
    - "classification with an edge improving semantic image segmentation with boundary detection"
    - "pyramid scene parsing network"

Connected through hyperedge HE-5 (induced by source paper "learning aerial image segmentation from online maps"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "fully convolutional networks for semantic segmentation"

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)