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 blindly assess image quality in the laboratory and wild"
  Abstract: Previous models for blind image quality assessment (BIQA) can only be trained (or fine-tuned) on one subject-rated database due to the difficulty of combining multiple databases with different perceptual scales. As a result, models trained in a well-controlled laboratory environment with synthetic distortions fail to generalize to realistic distortions, whose data distribution is different. Similarly, models optimized for images captured in the wild do not account for images simulated in the laboratory. Here we describe a simple technique of training BIQA models on multiple databases simultaneously without additional subjective testing for scale realignment. Specifically, we first create and combine image pairs within individual databases, whose ground-truth binary labels are computed from the corresponding mean opinion scores, indicating which of the two images is of higher quality. We then train a deep neural network for BIQA by learning-to-rank massive such image pairs. Extensive experiments on six databases demonstrate that our BIQA method based on the proposed learning technique works well for both synthetic and realistic distortions, outperforming existing BIQA models with a single set of model parameters. The generalizability of our method is further verified by group maximum differentiation (gMAD) competition.

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"
    - "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"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "end to end optimized image compression"

Connected through hyperedge HE-2 (induced by source paper "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"):
  with members:
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "learning transferable architectures for scalable image recognition"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning to detect multiple photographic defects"
    - "understanding how image quality affects deep neural networks"
    - "massive online crowdsourced study of subjective and objective picture quality"

Connected through hyperedge HE-3 (induced by source paper "from patches to pictures paq 2 piq mapping the perceptual space of picture quality"):
  with members:
    - "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"
    - "rankiqa learning from rankings for no reference image quality assessment"

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)