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: "zm net real time zero shot image manipulation network"
  Abstract: Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.

Connected through hyperedge HE-1 (induced by source paper "cross modality paired images generation for rgb infrared person re identification"):
  with members:
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "in defense of the triplet loss for person re identification"
    - "camera style adaptation for person re identification"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"
    - "disentangled person image generation"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "conditional neural style transfer with peer regularized feature transform"):
  with members:
    - "few shot unsupervised image to image translation"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "the relativistic discriminator a key element missing from standard gan"
    - "a style aware content loss for real time hd style transfer"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "multimodal unsupervised image to image translation"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"

Connected through hyperedge HE-3 (induced by source paper "styleremix an interpretable representation for neural image style transfer"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "style transfer by rigid alignment in neural net feature space"):
  with members:
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"
    - "characterizing and improving stability in neural style transfer"
    - "neural style transfer a review"

Connected through hyperedge HE-5 (induced by source paper "multi scale dynamic feature encoding network for image demoireing"):
  with members:
    - "moire photo restoration using multiresolution convolutional neural networks"
    - "demoireing of camera captured screen images using deep convolutional neural network"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "multimodal unsupervised image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "a closed form solution to photorealistic image stylization"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "xgan unsupervised image to image translation for many to many mappings"
    - "multi agent diverse generative adversarial networks"
    - "style transfer from non parallel text by cross alignment"
    - "decomposing motion and content for natural video sequence prediction"
    - "triangle generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "a style aware content loss for real time hd style transfer"):
  with members:
    - "stroke controllable fast style transfer with adaptive receptive fields"
    - "a variational u net for conditional appearance and shape generation"
    - "generative adversarial networks an overview"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "meta networks for neural style transfer"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-8 (induced by source paper "avatar net multi scale zero shot style transfer by feature decoration"):
  with members:
    - "stereoscopic neural style transfer"
    - "meta networks for neural style transfer"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "style transfer via texture synthesis"

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)