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: "a fully progressive approach to single image super resolution"
  Abstract: Recent deep learning approaches to single image super-resolution have achieved impressive results in terms of traditional error measures and perceptual quality. However, in each case it remains challenging to achieve high quality results for large upsampling factors. To this end, we propose a method (ProSR) that is progressive both in architecture and training: the network upsamples an image in intermediate steps, while the learning process is organized from easy to hard, as is done in curriculum learning. To obtain more photorealistic results, we design a generative adversarial network (GAN), named ProGanSR, that follows the same progressive multi-scale design principle. This not only allows to scale well to high upsampling factors (e.g., 8x) but constitutes a principled multi-scale approach that increases the reconstruction quality for all upsampling factors simultaneously. In particular ProSR ranks 2nd in terms of SSIM and 4th in terms of PSNR in the NTIRE2018 SISR challenge [34]. Compared to the top-ranking team, our model is marginally lower, but runs 5 times faster.

Connected through hyperedge HE-1 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches"):
  with members:
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "singan learning a generative model from a single natural image"
    - "cross x learning for fine grained visual categorization"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "learning to navigate for fine grained classification"
    - "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "learned multi view texture super resolution"):
  with members:
    - "3d appearance super resolution with deep learning"
    - "deep learning for single image super resolution a brief review"
    - "fast and accurate single image super resolution via information distillation network"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimedia super resolution via deep learning a survey"
    - "residual dense network for image super resolution"
    - "deep network interpolation for continuous imagery effect transition"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"

Connected through hyperedge HE-3 (induced by source paper "fine grained urban flow inference"):
  with members:
    - "urbanfm inferring fine grained urban flows"
    - "image super resolution using very deep residual channel attention networks"
    - "deepdpm dynamic population mapping via deep neural network"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-4 (induced by source paper "creating high resolution images with a latent adversarial generator"):
  with members:
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "the relativistic discriminator a key element missing from standard gan"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the perception distortion tradeoff"
    - "nima neural image assessment"
    - "learning to maintain natural image statistics"
    - "enhanced deep residual networks for single image super resolution"
    - "enhancenet single image super resolution through automated texture synthesis"

Connected through hyperedge HE-5 (induced by source paper "progressive learning and disentanglement of hierarchical representations"):
  with members:
    - "improving disentangled representation learning with the beta bernoulli process"
    - "isolating sources of disentanglement in variational autoencoders"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning hierarchical features from generative models"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "an architecture for deep hierarchical generative models"
    - "learning to generate with memory"
    - "deep generative image models using a laplacian pyramid of adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "self supervised learning of class embeddings from video"):
  with members:
    - "learning landmarks from unaligned data using image translation"
    - "growing a brain fine tuning by increasing model capacity"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "self supervised learning of a facial attribute embedding from video"
    - "self supervised spatiotemporal feature learning via video rotation prediction"
    - "unsupervised discovery of object landmarks as structural representations"
    - "voxceleb2 deep speaker recognition"
    - "self supervised video representation learning with space time cubic puzzles"

Connected through hyperedge HE-7 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "class aware fully convolutional gaussian and poisson denoising"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "regularization by denoising clarifications and new interpretations"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "image super resolution using very deep residual channel attention networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "deep image prior"

Connected through hyperedge HE-8 (induced by source paper "feedback network for image super resolution"):
  with members:
    - "fast and accurate single image super resolution via information distillation network"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "top down feedback for crowd counting convolutional neural network"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"
    - "learning a single convolutional super resolution network for multiple degradations"

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)