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 probabilistic quality representation approach to deep blind image quality prediction"
  Abstract: Blind image quality assessment (BIQA) remains a very challenging problem due to the unavailability of a reference image. Deep learning based BIQA methods have been attracting increasing attention in recent years, yet it remains a difficult task to train a robust deep BIQA model because of the very limited number of training samples with human subjective scores. Most existing methods learn a regression network to minimize the prediction error of a scalar image quality score. However, such a scheme ignores the fact that an image will receive divergent subjective scores from different subjects, which cannot be adequately represented by a single scalar number. This is particularly true on complex, real-world distorted images. Moreover, images may broadly differ in their distributions of assigned subjective scores. Recognizing this, we propose a new representation of perceptual image quality, called probabilistic quality representation (PQR), to describe the image subjective score distribution, whereby a more robust loss function can be employed to train a deep BIQA model. The proposed PQR method is shown to not only speed up the convergence of deep model training, but to also greatly improve the achievable level of quality prediction accuracy relative to scalar quality score regression methods. The source code is available at this https URL

Connected through hyperedge HE-1 (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"
    - "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"
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "bilinear cnn models for fine grained visual recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "hallucinated iqa no reference image quality assessment via adversarial learning"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "quality aware network for set to set recognition"
    - "weakly and self supervised learning for content aware deep image retargeting"
    - "learning deep cnn denoiser prior for image restoration"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "stacked hourglass networks for human pose estimation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (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"
    - "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"
    - "squared earth mover s distance based loss for training deep neural networks"

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)