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: "alive caricature from 2d to 3d"
  Abstract: Caricature is an art form that expresses subjects in abstract, simple and exaggerated view. While many caricatures are 2D images, this paper presents an algorithm for creating expressive 3D caricatures from 2D caricature images with a minimum of user interaction. The key idea of our approach is to introduce an intrinsic deformation representation that has a capacity of extrapolation enabling us to create a deformation space from standard face dataset, which maintains face constraints and meanwhile is sufficiently large for producing exaggerated face models. Built upon the proposed deformation representation, an optimization model is formulated to find the 3D caricature that captures the style of the 2D caricature image automatically. The experiments show that our approach has better capability in expressing caricatures than those fitting approaches directly using classical parametric face models such as 3DMM and FaceWareHouse. Moreover, our approach is based on standard face datasets and avoids constructing complicated 3D caricature training set, which provides great flexibility in real applications.

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"
    - "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"

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"
    - "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"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "digital twin acquiring high fidelity 3d avatar from a single image"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "disentangled representation learning for 3d face shape"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "3d guided fine grained face manipulation"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "nonlinear 3d face morphable model"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "disentangled representation learning for 3d face shape"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "nonlinear 3d face morphable model"
    - "sparse data driven mesh deformation"
    - "3d face morphable models in the wild"
    - "geometric deep learning going beyond euclidean data"
    - "mesh based autoencoders for localized deformation component analysis"

Connected through hyperedge HE-5 (induced by source paper "learning 3d human body embedding"):
  with members:
    - "disentangled representation learning for 3d face shape"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-6 (induced by source paper "caricatureshop personalized and photorealistic caricature sketching"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "super fan integrated facial landmark localization and super resolution of real world low resolution faces in arbitrary poses with gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "chessboard and chess piece recognition with the support of neural networks"):
  with members:
    - "weakly supervised localization using deep feature maps"
    - "large scale analysis of chess games with chess engines a preliminary report"
    - "hybridization of otsu method and median filter for color image segmentation"
    - "chess quick and robust detection of chess board features"

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)