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: "cascaded regressor based 3d face reconstruction from a single arbitrary view image"
  Abstract: State-of-the-art methods reconstruct three-dimensional (3D) face shapes from a single image by fitting 3D face models to input images or by directly learning mapping functions between two-dimensional (2D) images and 3D faces. However, they are often difficult to use in real-world applications due to expensive online optimization or to the requirement of frontal face images. This paper approaches the 3D face reconstruction problem as a regression problem rather than a model fitting problem. Given an input face image along with some pre-defined facial landmarks on it, a series of shape adjustments to the initial 3D face shape are computed through cascaded regressors based on the deviations between the input landmarks and the landmarks obtained from the reconstructed 3D faces. The cascaded regressors are offline learned from a set of 3D faces and their corresponding 2D face images in various views. By treating the landmarks that are invisible in large view angles as missing data, the proposed method can handle arbitrary view face images in a unified way with the same regressors. Experiments on the BFM and Bosphorus databases demonstrate that the proposed method can reconstruct 3D faces from arbitrary view images more efficiently and more accurately than existing methods.

Connected through hyperedge HE-1 (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-2 (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"
    - "deepsketch2face a deep learning based sketching system for 3d face and caricature modeling"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

Connected through hyperedge HE-3 (induced by source paper "unrestricted facial geometry reconstruction using image to image translation"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d face reconstruction with geometry details from a single image"
    - "unsupervised cross domain image generation"
    - "learning from simulated and unsupervised images through adversarial training"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "image to image translation with conditional adversarial networks"
    - "customized facial constant positive air pressure cpap masks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

Connected through hyperedge HE-4 (induced by source paper "learning detailed face reconstruction from a single image"):
  with members:
    - "convolutional pose machines"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "fine scale surface normal estimation using a single nir image"
    - "3d face reconstruction by learning from synthetic data"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "3d face reconstruction via cascaded regression in shape space"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-5 (induced by source paper "3d face reconstruction by learning from synthetic data"):
  with members:
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "3d face reconstruction via cascaded regression in shape space"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "multi view 3d models from single images with a convolutional network"):
  with members:
    - "generating images with perceptual similarity metrics based on deep networks"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "deep convolutional inverse graphics network"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "deep multi scale video prediction beyond mean square error"
    - "3d face reconstruction via cascaded regression in shape space"

Connected through hyperedge HE-7 (induced by source paper "single view to multi view reconstructing unseen views with a convolutional network"):
  with members:
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "3d shapenets a deep representation for volumetric shapes"
    - "learning to generate chairs with convolutional neural networks"
    - "depth map prediction from a single image using a multi scale deep network"
    - "fully convolutional networks for semantic segmentation"

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)