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: "gp gan towards realistic high resolution image blending"
  Abstract: It is common but challenging to address high-resolution image blending in the automatic photo editing application. In this paper, we would like to focus on solving the problem of high-resolution image blending, where the composite images are provided. We propose a framework called Gaussian-Poisson Generative Adversarial Network (GP-GAN) to leverage the strengths of the classical gradient-based approach and Generative Adversarial Networks. To the best of our knowledge, it's the first work that explores the capability of GANs in high-resolution image blending task. Concretely, we propose Gaussian-Poisson Equation to formulate the high-resolution image blending problem, which is a joint optimization constrained by the gradient and color information. Inspired by the prior works, we obtain gradient information via applying gradient filters. To generate the color information, we propose a Blending GAN to learn the mapping between the composite images and the well-blended ones. Compared to the alternative methods, our approach can deliver high-resolution, realistic images with fewer bleedings and unpleasant artifacts. Experiments confirm that our approach achieves the state-of-the-art performance on Transient Attributes dataset. A user study on Amazon Mechanical Turk finds that the majority of workers are in favor of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy"):
  with members:
    - "a layer based sequential framework for scene generation with gans"
    - "how generative adversarial networks and their variants work an overview"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "quick and easy time series generation with established image based gans"
    - "neuroscore a brain inspired evaluation metric for generative adversarial networks"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "the gan landscape losses architectures regularization and normalization"
    - "deep semantic hashing with generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "measures to evaluate generative adversarial networks based on direct analysis of generated images"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "pros and cons of gan evaluation measures"
    - "perceptual adversarial networks for image to image transformation"
    - "perceptual generative adversarial networks for small object detection"
    - "improved training of wasserstein gans"
    - "dualgan unsupervised dual learning for image to image translation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "object discovery with a copy pasting gan"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "inserting videos into videos"
    - "monet unsupervised scene decomposition and representation"
    - "unsupervised moving object detection via contextual information separation"
    - "multi object representation learning with iterative variational inference"
    - "learning to generate synthetic data via compositing"
    - "unsupervised image matching and object discovery as optimization"
    - "learning to segment via cut and paste"

Connected through hyperedge HE-4 (induced by source paper "adaptive composition gan towards realistic image synthesis"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "few shot unsupervised image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "compositional gan learning conditional image composition"
    - "multimodal unsupervised image to image translation"
    - "gan dissection visualizing and understanding generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "ismo gan adversarial learning for monocular non rigid 3d reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "generative image inpainting with contextual attention"
    - "octnetfusion learning depth fusion from data"
    - "progressive growing of gans for improved quality stability and variation"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "better together joint reasoning for non rigid 3d reconstruction with specularities and shading"

Connected through hyperedge HE-6 (induced by source paper "face x ray for more general face forgery detection"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "on the generalization of gan image forensics"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "celeb df a new dataset for deepfake forensics"
    - "incremental learning for the detection and classification of gan generated images"

Connected through hyperedge HE-7 (induced by source paper "improving the harmony of the composite image by spatial separated attention module"):
  with members:
    - "inserting videos into videos"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "selective kernel networks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "temporally coherent video harmonization using adversarial networks"
    - "attention gan for object transfiguration in wild images"
    - "free form image inpainting with gated convolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-8 (induced by source paper "fsgan subject agnostic face swapping and reenactment"):
  with members:
    - "faceforensics learning to detect manipulated facial images"
    - "cr gan learning complete representations for multi view generation"
    - "reenactgan learning to reenact faces via boundary transfer"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "ganimation anatomically aware facial animation from a single image"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "fsnet an identity aware generative model for image based face swapping"
    - "vggface2 a dataset for recognising faces across pose and age"

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)