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 "nima neural image assessment"):
  Abstract: Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the  mean  opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the  distribution  of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware,  no-reference  quality assessment.

Includes members:
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "on the use of deep learning for blind image quality 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 "learned perceptual image enhancement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "super resolving commercial satellite imagery using realistic training data")
  Member "a probabilistic quality representation approach to deep blind image quality prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to blindly assess image quality in the laboratory and wild")
  Member "mobilenets efficient convolutional neural networks for mobile vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "a color intensity invariant low level feature optimization framework for image quality assessment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the use of deep learning for blind image quality assessment")
  Member "visual aesthetic quality assessment with multi task deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross connected networks for multi task learning of detection and segmentation")
  Member "brain inspired deep networks for image aesthetics assessment":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep cross networks with aesthetic preference for cross domain recommendation")

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)