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: "dipiq blind image quality assessment by learning to rank discriminable image pairs"
  Abstract: Objective assessment of image quality is fundamentally important in many image processing tasks. In this paper, we focus on learning blind image quality assessment (BIQA) models, which predict the quality of a digital image with no access to its original pristine-quality counterpart as reference. One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training. Such data are typically collected via subjective testing, which is cumbersome, slow, and expensive. Here, we first show that a vast amount of reliable training data in the form of quality-discriminable image pairs (DIPs) can be obtained automatically at low cost by exploiting large-scale databases with diverse image content. We then learn an opinion-unaware BIQA (OU-BIQA, meaning that no subjective opinions are used for training) model using RankNet, a pairwise learning-to-rank (L2R) algorithm, from millions of DIPs, each associated with a perceptual uncertainty level, leading to a DIP inferred quality (dipIQ) index. Extensive experiments on four benchmark IQA databases demonstrate that dipIQ outperforms the state-of-the-art OU-BIQA models. The robustness of dipIQ is also significantly improved as confirmed by the group MAximum Differentiation competition method. Furthermore, we extend the proposed framework by learning models with ListNet (a listwise L2R algorithm) on quality-discriminable image lists (DIL). The resulting DIL inferred quality index achieves an additional performance gain.

Connected through hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (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-3 (induced by source paper "intrinsic image popularity assessment"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "how to become instagram famous post popularity prediction with dual attention"
    - "pay attention to virality understanding popularity of social media videos with the attention mechanism"
    - "sequential prediction of social media popularity with deep temporal context networks"
    - "cats and captions vs creators and the clock comparing multimodal content to context in predicting relative popularity"
    - "modeling image virality with pairwise spatial transformer networks"
    - "time matters multi scale temporalization of social media popularity"
    - "predicting popularity of online videos using support vector regression"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "a probabilistic quality representation approach to deep blind image quality prediction"
    - "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"
    - "stacked hourglass networks for human pose estimation"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-6 (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"
    - "mask r cnn"
    - "mean deviation similarity index efficient and reliable full reference image quality evaluator"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "resumenet a learning based framework for automatic resume quality assessment"):
  with members:
    - "deepqoe a unified framework for learning to predict video qoe"
    - "universal sentence encoder"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "neural aggregation network for video face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "fractional max pooling"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "a perceptually weighted rank correlation indicator for objective image quality assessment"):
  with members:
    - "quality aware network for set to set recognition"
    - "empirical analysis of predictive algorithms for collaborative filtering"

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)