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: "webcaricature a benchmark for caricature face recognition"
  Abstract: Studying caricature recognition is fundamentally important to understanding of face perception. However, little research has been conducted in the computer vision community, largely due to the shortage of suitable datasets. In this paper, a new caricature dataset is built, with the objective to facilitate research in caricature recognition. All the caricatures and face images were collected from the Web. Compared with two existing datasets, this dataset is much more challenging, with a much greater number of available images, artistic styles and larger intra-personal variations. Evaluation protocols are also offered together with their baseline performances on the dataset to allow fair comparisons. Besides, a framework for caricature face recognition is presented to make a thorough analyze of the challenges of caricature recognition. By analyzing the challenges, the goal is to show problems that worth to be further investigated. Additionally, based on the evaluation protocols and the framework, baseline performances of various state-of-the-art algorithms are provided. A conclusion is that there is still a large space for performance improvement and the analyzed problems still need further investigation.

Connected through hyperedge HE-1 (induced by source paper "mw gan multi warping gan for caricature generation with multi style geometric exaggeration"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "warpgan automatic caricature generation"
    - "multimodal unsupervised image to image translation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "triangle generative adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-2 (induced by source paper "3d magic mirror automatic video to 3d caricature translation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "multimodal unsupervised image to image translation"
    - "alive caricature from 2d to 3d"
    - "3d face reconstruction with geometry details from a single image"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-3 (induced by source paper "icartoonface a benchmark of cartoon person recognition"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning transferable architectures for scalable image recognition"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "billion scale similarity search with gpus"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "warpgan automatic caricature generation"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "caricatureshop personalized and photorealistic caricature sketching"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "soft gated warping gan for pose guided person image synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "multimodal unsupervised image to image translation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-5 (induced by source paper "carigan caricature generation through weakly paired adversarial learning"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "stochastic adversarial video prediction"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "began boundary equilibrium generative adversarial networks"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "alive caricature from 2d to 3d"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "sparse data driven mesh deformation"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"

Connected through hyperedge HE-7 (induced by source paper "deep cross modal learning for caricature verification and identification cavinet"):
  with members:
    - "understanding neural networks through deep visualization"
    - "facenet a unified embedding for face recognition and clustering"
    - "going deeper with convolutions"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)