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: "nima neural image assessment"
  Abstract: Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the  mean  opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the  distribution  of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware,  no-reference  quality assessment.

Connected through hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (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"
    - "a fully progressive approach to single image super resolution"
    - "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-3 (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"
    - "learned perceptual image enhancement"
    - "learning a single convolutional super resolution network for multiple degradations"
    - "super resolution for overhead imagery using densenets and adversarial learning"
    - "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-4 (induced by source paper "binocular rivalry oriented predictive auto encoding network for blind stereoscopic image quality assessment"):
  with members:
    - "stereoscopic omnidirectional image quality assessment based on predictive coding theory"
    - "end to end optimized image compression"
    - "identity mappings in deep residual networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "density modeling of images using a generalized normalization transformation"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "aesthetic quality assessment for group photograph"):
  with members:
    - "an universal image attractiveness ranking framework"
    - "ilgnet inception modules with connected local and global features for efficient image aesthetic quality classification using domain adaptation"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "will people like your image learning the aesthetic space"
    - "the beauty of capturing faces rating the quality of digital portraits"

Connected through hyperedge HE-6 (induced by source paper "subjective quality assessment for youtube ugc dataset"):
  with members:
    - "youtube ugc dataset for video compression research"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-7 (induced by source paper "a cnn based blind denoising method for endoscopic images"):
  with members:
    - "real world noisy image denoising a new benchmark"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "unsupervised image translation using adversarial networks for improved plant disease recognition"):
  with members:
    - "generative adversarial networks an overview"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "adversarial nets with perceptual losses for text to image synthesis"
    - "dualgan unsupervised dual learning for image to image translation"

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)