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: "automatic content aware color and tone stylization"
  Abstract: We introduce a new technique that automatically generates diverse, visually compelling stylizations for a photograph in an unsupervised manner. We achieve this by learning style ranking for a given input using a large photo collection and selecting a diverse subset of matching styles for final style transfer. We also propose a novel technique that transfers the global color and tone of the chosen exemplars to the input photograph while avoiding the common visual artifacts produced by the existing style transfer methods. Together, our style selection and transfer techniques produce compelling, artifact-free results on a wide range of input photographs, and a user study shows that our results are preferred over other techniques.

Connected through hyperedge HE-1 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "ai benchmark running deep neural networks on android smartphones"
    - "fast perceptual image enhancement"

Connected through hyperedge HE-2 (induced by source paper "supervised and unsupervised learning of parameterized color enhancement"):
  with members:
    - "deep bilateral learning for real time image enhancement"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "exposure a white box photo post processing framework"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "automatic photo adjustment using deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "image harmonization datasets hcoco hadobe5k hflickr and hday2night"):
  with members:
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep image harmonization"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "learning a discriminative model for the perception of realism in composite images"

Connected through hyperedge HE-4 (induced by source paper "gradient based camera exposure control for outdoor mobile platforms"):
  with members:
    - "ssd single shot multibox detector"
    - "partial sum minimization of singular values in robust pca algorithm and applications"

Connected through hyperedge HE-5 (induced by source paper "manipulating attributes of natural scenes via hallucination"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "distort and recover color enhancement using deep reinforcement learning"):
  with members:
    - "image aesthetic assessment an experimental survey"
    - "image to image translation with conditional adversarial networks"
    - "photo aesthetics ranking network with attributes and content adaptation"
    - "deep reinforcement learning with double q learning"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "automatic photo adjustment using deep neural networks"
    - "cnn features off the shelf an astounding baseline for recognition"

Connected through hyperedge HE-7 (induced by source paper "exposure a white box photo post processing framework"):
  with members:
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "deep bilateral learning for real time image enhancement"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "creatism a deep learning photographer capable of creating professional work"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-8 (induced by source paper "aesthetic driven image enhancement by adversarial learning"):
  with members:
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "quantitative analysis of automatic image cropping algorithms a dataset and comparative study"
    - "image aesthetic assessment an experimental survey"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "learning to compose with professional photographs on the web"
    - "untrimmednets for weakly supervised action recognition and detection"
    - "characterizing and improving stability in neural style transfer"
    - "a lamp adaptive layout aware multi patch deep convolutional neural network for photo aesthetic 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)