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: "pixelnn example based image synthesis"
  Abstract: We present a simple nearest-neighbor (NN) approach that synthesizes high-frequency photorealistic images from an "incomplete" signal such as a low-resolution image, a surface normal map, or edges. Current state-of-the-art deep generative models designed for such conditional image synthesis lack two important things: (1) they are unable to generate a large set of diverse outputs, due to the mode collapse problem. (2) they are not interpretable, making it difficult to control the synthesized output. We demonstrate that NN approaches potentially address such limitations, but suffer in accuracy on small datasets. We design a simple pipeline that combines the best of both worlds: the first stage uses a convolutional neural network (CNN) to maps the input to a (overly-smoothed) image, and the second stage uses a pixel-wise nearest neighbor method to map the smoothed output to multiple high-quality, high-frequency outputs in a controllable manner. We demonstrate our approach for various input modalities, and for various domains ranging from human faces to cats-and-dogs to shoes and handbags.

Connected through hyperedge HE-1 (induced by source paper "unpaired pose guided human image generation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "soft gated warping gan for pose guided person image synthesis"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised person image synthesis in arbitrary poses"
    - "toward characteristic preserving image based virtual try on network"
    - "multimodal unsupervised image to image translation"
    - "dense pose transfer"
    - "everybody dance now"

Connected through hyperedge HE-2 (induced by source paper "shapes and context in the wild image synthesis manipulation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "recycle gan unsupervised video retargeting"
    - "large scale gan training for high fidelity natural image synthesis"
    - "semi parametric image synthesis"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "cycada cycle consistent adversarial domain adaptation"
    - "expecting the unexpected training detectors for unusual pedestrians with adversarial imposters"

Connected through hyperedge HE-3 (induced by source paper "semantic example guided image to image translation"):
  with members:
    - "asymmetric gan for unpaired image to image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "generative image inpainting with contextual attention"
    - "multi agent diverse generative adversarial networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "generative creativity adversarial learning for bionic design"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "design design inspiration from generative networks"
    - "multimodal unsupervised image to image translation"
    - "deep painterly harmonization"
    - "multi agent diverse generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "semantic image synthesis via adversarial learning"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-6 (induced by source paper "smit stochastic multi label image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "dynamic multimodal instance segmentation guided by natural language queries"
    - "recycle gan unsupervised video retargeting"
    - "the relativistic discriminator a key element missing from standard gan"
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "semantic aware grad gan for virtual to real urban scene adaption"):
  with members:
    - "demystifying neural style transfer"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "unsupervised image to image translation networks"
    - "squeeze and excitation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "recycle gan unsupervised video retargeting"):
  with members:
    - "unsupervised video to video translation"
    - "multimodal unsupervised image to image translation"
    - "probabilistic video generation using holistic attribute control"
    - "learning to generate long term future via hierarchical prediction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised learning of depth and ego motion from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "actionvlad learning spatio temporal aggregation for action classification"

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)