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: "perceptual quality prediction on authentically distorted images using a bag of features approach"
  Abstract: Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore they learn image features that effectively predict human visual quality judgments of inauthentic, and usually isolated (single) distortions. However, real-world images usually contain complex, composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images, in different color spaces and transform domains. We propose a bag of feature-maps approach which avoids assumptions about the type of distortion(s) contained in an image, focusing instead on capturing consistencies, or departures therefrom, of the statistics of real world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features towards improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good quality prediction power that is better than other leading models.

Connected through hyperedge HE-1 (induced by source paper "a comparative evaluation of temporal pooling methods for blind video quality assessment"):
  with members:
    - "quality assessment of in the wild videos"
    - "modeling the time varying subjective quality of http video streams with rate adaptations"

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 "no reference light field image quality assessment based on micro lens image"):
  with members:
    - "towards a quality metric for dense light fields"

Connected through hyperedge HE-4 (induced by source paper "no reference quality assessment of contrast distorted images using contrast enhancement"):
  with members:
    - "learning a no reference quality assessment model of enhanced images with big data"

Connected through hyperedge HE-5 (induced by source paper "a probabilistic quality representation approach to deep blind image quality prediction"):
  with members:
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "an attention driven approach of no reference image quality assessment"
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

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)