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: "subjective and objective quality assessment of image a survey"
  Abstract: With the increasing demand for image-based applications, the efficient and reliable evaluation of image quality has increased in importance. Measuring the image quality is of fundamental importance for numerous image processing applications, where the goal of image quality assessment (IQA) methods is to automatically evaluate the quality of images in agreement with human quality judgments. Numerous IQA methods have been proposed over the past years to fulfill this goal. In this paper, a survey of the quality assessment methods for conventional image signals, as well as the newly emerged ones, which includes the high dynamic range (HDR) and 3-D images, is presented. A comprehensive explanation of the subjective and objective IQA and their classification is provided. Six widely used subjective quality datasets, and performance measures are reviewed. Emphasis is given to the full-reference image quality assessment (FR-IQA) methods, and 9 often-used quality measures (including mean squared error (MSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), visual information fidelity (VIF), most apparent distortion (MAD), feature similarity measure (FSIM), feature similarity measure for color images (FSIMC), dynamic range independent measure (DRIM), and tone-mapped images quality index (TMQI)) are carefully described, and their performance and computation time on four subjective quality datasets are evaluated. Furthermore, a brief introduction to 3-D IQA is provided and the issues related to this area of research are reviewed.

Connected through hyperedge HE-1 (induced by source paper "a smart system for selection of optimal product images in e commerce"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "item popularity prediction in e commerce using image quality feature vectors"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "rethinking the inception architecture for computer vision"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "no reference color image quality assessment from entropy to perceptual quality"):
  with members:
    - "learning a no reference quality assessment model of enhanced images with big data"
    - "efficient no reference quality assessment and classification model for contrast distorted images"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-3 (induced by source paper "image aesthetic assessment an experimental survey"):
  with members:
    - "hybrid cnn and dictionary based models for scene recognition and domain adaptation"
    - "visual aesthetic quality assessment with multi task deep learning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "brain inspired deep networks for image aesthetics assessment"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "describing human aesthetic perception by deeply learned attributes from flickr"
    - "polynet a pursuit of structural diversity in very deep networks"
    - "photo aesthetics ranking network with attributes and content adaptation"

Connected through hyperedge HE-4 (induced by source paper "semi fragile image authentication based on cfd and 3 bit quantization"):
  with members:
    - "iterative decoding of low density parity check codes a survey"

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.MM(Multimedia)