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: "3d face reconstruction via cascaded regression in shape space"
  Abstract: Cascaded regression has been recently applied to reconstructing 3D faces from single 2D images directly in shape space, and achieved state-of-the-art performance. This paper investigates thoroughly such cascaded regression based 3D face reconstruction approaches from four perspectives that are not well studied yet: (i) The impact of the number of 2D landmarks; (ii) the impact of the number of 3D vertices; (iii) the way of using standalone automated landmark detection methods; and (iv) the convergence property. To answer these questions, a simplified cascaded regression based 3D face reconstruction method is devised, which can be integrated with standalone automated landmark detection methods and reconstruct 3D face shapes that have the same pose and expression as the input face images, rather than normalized pose and expression. Moreover, an effective training method is proposed by disturbing the automatically detected landmarks. Comprehensive evaluation experiments have been done with comparison to other 3D face reconstruction methods. The results not only deepen the understanding of cascaded regression based 3D face reconstruction approaches, but also prove the effectiveness of proposed method.

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 "recent progress of face image synthesis"):
  with members:
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "random sampling and locality constraint for face sketch"
    - "face aging with conditional generative adversarial networks"
    - "srefi synthesis of realistic example face images"
    - "genegan learning object transfiguration and attribute subspace from unpaired data"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "multi view image generation from a single view"

Connected through hyperedge HE-3 (induced by source paper "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep neural networks"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "production level facial performance capture using deep convolutional neural networks"
    - "deep cascaded bi network for face hallucination"
    - "robust lstm autoencoders for face de occlusion in the wild"

Connected through hyperedge HE-4 (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-5 (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"
    - "cascaded regressor based 3d face reconstruction from a single arbitrary view image"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "effective face frontalization in unconstrained images"

Connected through hyperedge HE-6 (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"
    - "cascaded regressor based 3d face reconstruction from a single arbitrary view image"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (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"
    - "cascaded regressor based 3d face reconstruction from a single arbitrary view image"
    - "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"

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)