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: "massive online crowdsourced study of subjective and objective picture quality"
  Abstract: Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at  http://live.ece.utexas.edu/research/ChallengeDB/index.html .

Connected through hyperedge HE-1 (induced by source paper "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"):
  with members:
    - "learning to blindly assess image quality in the laboratory and wild"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "learning transferable architectures for scalable image recognition"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to detect multiple photographic defects"
    - "understanding how image quality affects deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "from patches to pictures paq 2 piq mapping the perceptual space of picture quality"):
  with members:
    - "learning to blindly assess image quality in the laboratory and wild"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "nima neural image assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "on the use of deep learning for blind image quality assessment"

Connected through hyperedge HE-3 (induced by source paper "learning to blindly assess image quality in the laboratory and wild"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "perceptually optimized image rendering"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "designing evaluations of machine learning models for subjective inference the case of sentence toxicity"):
  with members:
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "datasheets for datasets"
    - "on formalizing fairness in prediction with machine learning"
    - "like trainer like bot inheritance of bias in algorithmic content moderation"
    - "fairness in machine learning lessons from political philosophy"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-5 (induced by source paper "an universal image attractiveness ranking framework"):
  with members:
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "the unreasonable effectiveness of deep features as a perceptual metric"):
  with members:
    - "nima neural image assessment"
    - "learned perceptual image enhancement"
    - "enhanced deep residual networks for single image super resolution"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "video frame interpolation via adaptive separable convolution"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-7 (induced by source paper "blind predicting similar quality map for image quality assessment"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "koniq 10k towards an ecologically valid and large scale iqa database"
    - "on the use of deep learning for blind image quality assessment"
    - "focal loss for dense object detection"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "mask r cnn"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"

Connected through hyperedge HE-8 (induced by source paper "nima neural image assessment"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "on the use of deep learning for blind image quality assessment"
    - "learned perceptual image enhancement"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a color intensity invariant low level feature optimization framework for image quality assessment"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "brain inspired deep networks for image aesthetics assessment"

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)