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: "learning detailed face reconstruction from a single image"
  Abstract: Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. The reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. The proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). The proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. Therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. The accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.

Connected through hyperedge HE-1 (induced by source paper "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "audio driven talking face video generation with learning based personalized head pose"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "neural voice puppetry audio driven facial reenactment"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "lip movements generation at a glance"

Connected through hyperedge HE-3 (induced by source paper "audio driven talking face video generation with natural head pose"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "few shot adversarial learning of realistic neural talking head models"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "talking face generation by adversarially disentangled audio visual representation"

Connected through hyperedge HE-4 (induced by source paper "towards high fidelity 3d face reconstruction from in the wild images using graph convolutional networks"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "photo realistic facial details synthesis from single image"
    - "face to parameter translation for game character auto creation"
    - "multi prototype networks for unconstrained set based face recognition"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "mvf net multi view 3d face morphable model regression"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"

Connected through hyperedge HE-5 (induced by source paper "joint face alignment and 3d face reconstruction with application to face recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "densereg fully convolutional dense shape regression in the wild"
    - "nonlinear 3d face morphable model"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "pose invariant face alignment with a single cnn"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "robust face alignment using a mixture of invariant experts"
    - "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"

Connected through hyperedge HE-6 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-7 (induced by source paper "ddsl deep differentiable simplex layer for learning geometric signals"):
  with members:
    - "spherical cnns on unstructured grids"
    - "convolutional neural networks on non uniform geometrical signals using euclidean spectral transformation"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "learning category specific mesh reconstruction from image collections"
    - "texturenet consistent local parametrizations for learning from high resolution signals on meshes"
    - "unsupervised training for 3d morphable model regression"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"

Connected through hyperedge HE-8 (induced by source paper "3dfacegan adversarial nets for 3d face representation generation and translation"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "began boundary equilibrium generative adversarial networks"
    - "deriving neural architectures from sequence and graph kernels"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "progressive growing of gans for improved quality stability and variation"

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)