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 "koniq 10k an ecologically valid database for deep learning of blind image quality assessment"):
  Abstract: Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content, and annotating it accurately. We present a systematic and scalable approach to create KonIQ-10k, the largest IQA dataset to date consisting of 10,073 quality scored images. This is the first in-the-wild database aiming for ecological validity, with regard to the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). A correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.

Includes members:
  Member "learning to blindly assess image quality in the laboratory and wild":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "nima neural image assessment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "on the use of deep learning for blind image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "rankiqa learning from rankings for no reference image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment")
  Member "inception v4 inception resnet and the impact of residual connections on learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "learning to detect multiple photographic defects":
    also connected through other hyperedges:
      (none)
  Member "understanding how image quality affects deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise")

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)