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: "learning to rank for blind image quality assessment"
  Abstract: Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, the acquisition of image quality scores has several limitations: 1) scores are not precise, because subjects are usually uncertain about which score most precisely represents the perceptual quality of a given image; 2) subjective judgements of quality may be biased by image content; 3) the quality scales between different distortion categories are inconsistent; and 4) it is challenging to obtain a large scale database, or to extend existing databases, because of the inconvenience of collecting sufficient images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs such as "the quality of image Ia is better than that of image Ib" for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso (MKLGL) to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves comparable performance to state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

Connected through hyperedge HE-1 (induced by source paper "exploiting unlabeled data in cnns by self supervised learning to rank"):
  with members:
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "iterative crowd counting"
    - "on the use of deep learning for blind image quality assessment"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-2 (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"
    - "massive online crowdsourced study of subjective and objective picture quality"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-3 (induced by source paper "learning a no reference quality assessment model of enhanced images with big data"):
  with members:
    - "brain inspired deep networks for image aesthetics assessment"

Connected through hyperedge HE-4 (induced by source paper "rankiqa learning from rankings for no reference image quality assessment"):
  with members:
    - "on the use of deep learning for blind image quality assessment"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "learning to compare image patches via convolutional neural networks"
    - "deep metric learning via lifted structured feature embedding"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "automatic image cropping for visual aesthetic enhancement using deep neural networks and cascaded regression"):
  with members:
    - "object discovery via cohesion measurement"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-6 (induced by source paper "dipiq blind image quality assessment by learning to rank discriminable image pairs"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

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)