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: "image to image translation via group wise deep whitening and coloring transformation"
  Abstract: Recently, unsupervised exemplar-based image-to-image translation, conditioned on a given exemplar without the paired data, has accomplished substantial advancements. In order to transfer the information from an exemplar to an input image, existing methods often use a normalization technique, e.g., adaptive instance normalization, that controls the channel-wise statistics of an input activation map at a particular layer, such as the mean and the variance. Meanwhile, style transfer approaches similar task to image translation by nature, demonstrated superior performance by using the higher-order statistics such as covariance among channels in representing a style. In detail, it works via whitening (given a zero-mean input feature, transforming its covariance matrix into the identity). followed by coloring (changing the covariance matrix of the whitened feature to those of the style feature). However, applying this approach in image translation is computationally intensive and error-prone due to the expensive time complexity and its non-trivial backpropagation. In response, this paper proposes an end-to-end approach tailored for image translation that efficiently approximates this transformation with our novel regularization methods. We further extend our approach to a group-wise form for memory and time efficiency as well as image quality. Extensive qualitative and quantitative experiments demonstrate that our proposed method is fast, both in training and inference, and highly effective in reflecting the style of an exemplar. Finally, our code is available at https://github.com/WonwoongCho/GDWCT.

Connected through hyperedge HE-1 (induced by source paper "ganilla generative adversarial networks for image to illustration translation"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "few shot unsupervised image to image translation"
    - "interactive sketch fill multiclass sketch to image translation"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "latent filter scaling for multimodal unsupervised image to image translation"
    - "sketchygan towards diverse and realistic sketch to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "unpaired image translation via adaptive convolution based normalization"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "conditional image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "instagan instance aware image to image translation"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "coloring with limited data few shot colorization via memory augmented networks"):
  with members:
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "coloring with words guiding image colorization through text based palette generation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-4 (induced by source paper "channel decomposition into painting actions"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "learning latent dynamics for planning from pixels"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "recurrent world models facilitate policy evolution"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "multi style generative network for real time transfer"

Connected through hyperedge HE-5 (induced by source paper "channel decomposition on generative networks"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "learning latent dynamics for planning from pixels"
    - "a style based generator architecture for generative adversarial networks"
    - "recurrent world models facilitate policy evolution"
    - "multi style generative network for real time transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "universal style transfer via feature transforms"

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)