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: "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
  Abstract: Face modeling has been paid much attention in the field of visual computing. There exist many scenarios, including cartoon characters, avatars for social media, 3D face caricatures as well as face-related art and design, where low-cost interactive face modeling is a popular approach especially among amateur users. In this paper, we propose a deep learning based sketching system for 3D face and caricature modeling. This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features. A novel CNN based deep regression network is designed for inferring 3D face models from 2D sketches. Our network fuses both CNN and shape based features of the input sketch, and has two independent branches of fully connected layers generating independent subsets of coefficients for a bilinear face representation. Our system also supports gesture based interactions for users to further manipulate initial face models. Both user studies and numerical results indicate that our sketching system can help users create face models quickly and effectively. A significantly expanded face database with diverse identities, expressions and levels of exaggeration is constructed to promote further research and evaluation of face modeling techniques.

Connected through hyperedge HE-1 (induced by source paper "3d carigan an end to end solution to 3d caricature generation from face photos"):
  with members:
    - "carigans unpaired photo to caricature translation"
    - "maskgan towards diverse and interactive facial image manipulation"
    - "carigan caricature generation through weakly paired adversarial learning"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "warpgan automatic caricature generation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "coherent point drift networks unsupervised learning of non rigid point set registration"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "so net self organizing network for point cloud analysis"
    - "an unsupervised learning model for deformable medical image registration"
    - "began boundary equilibrium generative adversarial networks"
    - "feastnet feature steered graph convolutions for 3d shape analysis"
    - "convolutional neural network architecture for geometric matching"
    - "foldingnet interpretable unsupervised learning on 3d point clouds"
    - "3d shape reconstruction from sketches via multi view convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "3d face modeling from diverse raw scan data"):
  with members:
    - "joint face alignment and 3d face reconstruction with application to face recognition"
    - "disentangled representation learning for 3d face shape"
    - "on learning 3d face morphable model from in the wild images"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning category specific mesh reconstruction from image collections"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "3d coded 3d correspondences by deep deformation"
    - "dense 3d face correspondence"

Connected through hyperedge HE-4 (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"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "deepsketchhair deep sketch based 3d hair modeling"):
  with members:
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "v2v posenet voxel to voxel prediction network for accurate 3d hand and human pose estimation from a single depth map"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "improved training of wasserstein gans"
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "photographic image synthesis with cascaded refinement networks"
    - "3d sketching using multi view deep volumetric prediction"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "cartoonish sketch based face editing in videos using identity deformation transfer"):
  with members:
    - "learning to forecast and refine residual motion for image to video generation"
    - "cr gan learning complete representations for multi view generation"
    - "mocogan decomposing motion and content for video generation"
    - "image to image translation with conditional adversarial networks"
    - "sketch based 3d shape retrieval using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "disentangling features in 3d face shapes for joint face reconstruction and recognition"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "sphereface deep hypersphere embedding for face recognition"
    - "end to end 3d face reconstruction with deep neural networks"
    - "pose invariant face alignment with a single cnn"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "nasal patches and curves for expression robust 3d face recognition"

Connected through hyperedge HE-8 (induced by source paper "shape from mask a deep learning based human body shape reconstruction from binary mask images"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "began boundary equilibrium generative adversarial networks"
    - "learning from synthetic humans"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "3d face reconstruction by learning from synthetic data"

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.GR(Graphics)