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: "texture synthesis using convolutional neural networks"
  Abstract: Here we introduce a new model of natural textures based on the feature spaces of convolutional neural networks optimised for object recognition. Samples from the model are of high perceptual quality demonstrating the generative power of neural networks trained in a purely discriminative fashion. Within the model, textures are represented by the correlations between feature maps in several layers of the network. We show that across layers the texture representations increasingly capture the statistical properties of natural images while making object information more and more explicit. The model provides a new tool to generate stimuli for neuroscience and might offer insights into the deep representations learned by convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "collaborative distillation for ultra resolution universal style transfer"):
  with members:
    - "relational knowledge distillation"
    - "multimodal style transfer via graph cuts"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unpaired high resolution and scalable style transfer using generative adversarial networks"
    - "a style aware content loss for real time hd style transfer"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "learning to compare relation semantic alignment for few shot learning"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "dense classification and implanting for few shot learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"

Connected through hyperedge HE-3 (induced by source paper "semantic pyramid for image generation"):
  with members:
    - "seeing what a gan cannot generate"
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "on the steerability of generative adversarial networks"
    - "image resizing by reconstruction from deep features"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "gan dissection visualizing and understanding generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-4 (induced by source paper "global texture enhancement for fake face detection in the wild"):
  with members:
    - "detecting gan generated fake images using co occurrence matrices"
    - "on the generalization of gan image forensics"
    - "fakespotter a simple baseline for spotting ai synthesized fake faces"
    - "exposing gan synthesized faces using landmark locations"
    - "detecting and simulating artifacts in gan fake images"
    - "incremental learning for the detection and classification of gan generated images"
    - "fakespotter a simple yet robust baseline for spotting ai synthesized fake faces"
    - "do gans leave artificial fingerprints"

Connected through hyperedge HE-5 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only"):
  with members:
    - "multi marginal wasserstein gan"
    - "object driven text to image synthesis via adversarial training"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "semantics disentangling for text to image generation"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "text2scene generating compositional scenes from textual descriptions"
    - "to create what you tell generating videos from captions"

Connected through hyperedge HE-6 (induced by source paper "photo realistic video prediction on natural videos of largely changing frames"):
  with members:
    - "disentangling propagation and generation for video prediction"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dual motion gan for future flow embedded video prediction"
    - "contextvp fully context aware video prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "video frame synthesis using deep voxel flow"
    - "unsupervised learning for physical interaction through video prediction"

Connected through hyperedge HE-7 (induced by source paper "endol2h deep super resolution for capsule endoscopy"):
  with members:
    - "frame recurrent video super resolution"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "on the effectiveness of least squares generative adversarial networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-8 (induced by source paper "learning texture invariant representation for domain adaptation of semantic segmentation"):
  with members:
    - "domain adaptation for semantic segmentation with maximum squares loss"
    - "domain randomization and pyramid consistency simulation to real generalization without accessing target domain data"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "domain adaptation for structured output via discriminative patch representations"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"

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)