human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  Abstract: The research in image quality assessment (IQA) has a long history, and significant progress has been made by leveraging recent advances in deep neural networks (DNNs). Despite high correlation numbers on existing IQA datasets, DNN-based models may be easily falsified in the group maximum differentiation (gMAD) competition with strong counterexamples being identified. Here we show that gMAD examples can be used to improve blind IQA (BIQA) methods. Specifically, we first pre-train a DNN-based BIQA model using multiple noisy annotators, and fine-tune it on multiple subject-rated databases of synthetically distorted images, resulting in a top-performing baseline model. We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD. The resulting gMAD examples are most likely to reveal the relative weaknesses of the baseline, and suggest potential ways for refinement. We query ground truth quality annotations for the selected images in a well controlled laboratory environment, and further fine-tune the baseline on the combination of human-rated images from gMAD and existing databases. This process may be iterated, enabling active and progressive fine-tuning from gMAD examples for BIQA. We demonstrate the feasibility of our active learning scheme on a large-scale unlabeled image set, and show that the fine-tuned method achieves improved generalizability in gMAD, without destroying performance on previously trained databases.

Includes members:
  Member "i am going mad maximum discrepancy competition for comparing classifiers adaptively":
    also connected through other hyperedges:
      (none)
  Member "learning to blindly assess image quality in the laboratory and wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "koniq 10k an ecologically valid database for deep learning of blind image quality assessment")
  Member "deep neural networks for no reference and full reference image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "print defect mapping with semantic segmentation")
  Member "nima neural image assessment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "creating high resolution images with a latent adversarial generator")
  Member "on the use of deep learning for blind image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting unlabeled data in cnns by self supervised learning to rank")
  Member "pieapp perceptual image error assessment through pairwise preference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep journey into super resolution a survey")
  Member "rankiqa learning from rankings for no reference image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "make a face towards arbitrary high fidelity face manipulation")
  Member "dipiq blind image quality assessment by learning to rank discriminable image pairs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "exploiting unlabeled data in cnns by self supervised learning to rank")

Question: Please tell me which class the source paper 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)