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: "deep photo style transfer"
  Abstract: This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style. Our approach builds upon the recent work on painterly transfer that separates style from the content of an image by considering different layers of a neural network. However, as is, this approach is not suitable for photorealistic style transfer. Even when both the input and reference images are photographs, the output still exhibits distortions reminiscent of a painting. Our contribution is to constrain the transformation from the input to the output to be locally affine in colorspace, and to express this constraint as a custom fully differentiable energy term. We show that this approach successfully suppresses distortion and yields satisfying photorealistic style transfers in a broad variety of scenarios, including transfer of the time of day, weather, season, and artistic edits.

Connected through hyperedge HE-1 (induced by source paper "parameter free style projection for arbitrary style transfer"):
  with members:
    - "stylenas an empirical study of neural architecture search to uncover surprisingly fast end to end universal style transfer networks"
    - "fast universal style transfer for artistic and photorealistic rendering"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-2 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "black box adversarial attacks on video recognition models"
    - "adversarial t shirt evading person detectors in a physical world"
    - "semantic adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "constructing unrestricted adversarial examples with generative models"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

Connected through hyperedge HE-3 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "a closed form solution to photorealistic image stylization"
    - "the contextual loss for image transformation with non aligned data"
    - "pyramid embedded generative adversarial network for automated font generation"
    - "contained neural style transfer for decorated logo generation"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-4 (induced by source paper "imaginenet restyling apps using neural style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "learning linear transformations for fast arbitrary style transfer"
    - "demystifying neural style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "photorealistic style transfer with screened poisson equation"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "judge the judges a large scale evaluation study of neural language models for online review generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "on accurate evaluation of gans for language generation"
    - "neural text generation past present and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "texygen a benchmarking platform for text generation models"
    - "evaluating text gans as language models"

Connected through hyperedge HE-6 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"

Connected through hyperedge HE-7 (induced by source paper "self ensembling with gan based data augmentation for domain adaptation in semantic segmentation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-8 (induced by source paper "synthetic examples improve generalization for rare classes"):
  with members:
    - "low shot learning from imaginary 3d model"
    - "class balanced loss based on effective number of samples"
    - "an annotation saved is an annotation earned using fully synthetic training for object instance detection"
    - "learning category specific mesh reconstruction from image collections"
    - "syn2real a new benchmark forsynthetic to real visual domain adaptation"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "recognition in terra incognita"

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)