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: "perceptually optimized image rendering"
  Abstract: We develop a framework for rendering photographic images by directly optimizing their perceptual similarity to the original visual scene. Specifically, over the set of all images that can be rendered on a given display, we minimize the normalized Laplacian pyramid distance (NLPD), a measure of perceptual dissimilarity that is derived from a simple model of the early stages of the human visual system. When rendering images acquired with a higher dynamic range than that of the display, we find that the optimization boosts the contrast of low-contrast features without introducing significant artifacts, yielding results of comparable visual quality to current state-of-the-art methods, but without manual intervention or parameter adjustment. We also demonstrate the effectiveness of the framework for a variety of other display constraints, including limitations on minimum luminance (black point), mean luminance (as a proxy for energy consumption), and quantized luminance levels (halftoning). We show that the method may generally be used to enhance details and contrast, and, in particular, can be used on images degraded by optical scattering (e.g., fog). Finally, we demonstrate the necessity of each of the NLPD components-an initial power function, a multiscale transform, and local contrast gain control-in achieving these results and we show that NLPD is competitive with the current state-of-the-art image quality metrics.

Connected through hyperedge HE-1 (induced by source paper "enforcing perceptual consistency on generative adversarial networks by using the normalised laplacian pyramid distance"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "end to end optimization of nonlinear transform codes for perceptual quality"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"
    - "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-3 (induced by source paper "perceptnet a human visual system inspired neural network for estimating perceptual distance"):
  with members:
    - "enforcing perceptual consistency on generative adversarial networks by using the normalised laplacian pyramid distance"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "density modeling of images using a generalized normalization transformation"

Connected through hyperedge HE-4 (induced by source paper "compression for multiple reconstructions"):
  with members:
    - "system aware compression"
    - "optimized pre compensating compression"
    - "distributed functional scalar quantization simplified"
    - "distributed scalar quantization for computing high resolution analysis and extensions"

Connected through hyperedge HE-5 (induced by source paper "towards metamerism via foveated style transfer"):
  with members:
    - "sideeye a generative neural network based simulator of human peripheral vision"
    - "eigen distortions of hierarchical representations"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "object detection through search with a foveated visual system"
    - "perceptual losses for real time style transfer and super resolution"
    - "texture networks feed forward synthesis of textures and stylized images"

Connected through hyperedge HE-6 (induced by source paper "eigen distortions of hierarchical representations"):
  with members:
    - "a study and comparison of human and deep learning recognition performance under visual distortions"
    - "end to end optimized image compression"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)