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: "disentangling features in 3d face shapes for joint face reconstruction and recognition"
  Abstract: This paper proposes an encoder-decoder network to disentangle shape features during 3D face reconstruction from single 2D images, such that the tasks of reconstructing accurate 3D face shapes and learning discriminative shape features for face recognition can be accomplished simultaneously. Unlike existing 3D face reconstruction methods, our proposed method directly regresses dense 3D face shapes from single 2D images, and tackles identity and residual (i.e., non-identity) components in 3D face shapes explicitly and separately based on a composite 3D face shape model with latent representations. We devise a training process for the proposed network with a joint loss measuring both face identification error and 3D face shape reconstruction error. To construct training data we develop a method for fitting 3D morphable model (3DMM) to multiple 2D images of a subject. Comprehensive experiments have been done on MICC, BU3DFE, LFW and YTF databases. The results show that our method expands the capacity of 3DMM for capturing discriminative shape features and facial detail, and thus outperforms existing methods both in 3D face reconstruction accuracy and in face recognition accuracy.

Connected through hyperedge HE-1 (induced by source paper "transferable recognition aware image processing"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "report on ug 2 challenge track 1 assessing algorithms to improve video object detection and classification from unconstrained mobility platforms"
    - "bridging the gap between computational photography and visual recognition"
    - "super identity convolutional neural network for face hallucination"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "task driven super resolution object detection in low resolution images"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-2 (induced by source paper "on learning 3d face morphable model from in the wild images"):
  with members:
    - "representation learning by rotating your faces"
    - "face alignment in full pose range a 3d total solution"
    - "unsupervised training for 3d morphable model regression"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "learning dense facial correspondences in unconstrained images"
    - "extreme 3d face reconstruction looking past occlusions"

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"
    - "3d coded 3d correspondences by deep deformation"
    - "dense 3d face correspondence"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-4 (induced by source paper "on learning disentangled representations for gait recognition"):
  with members:
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "gait recognition via disentangled representation learning"
    - "retinaface single stage dense face localisation in the wild"
    - "synthesizing images of humans in unseen poses"
    - "towards highly accurate and stable face alignment for high resolution videos"
    - "arcface additive angular margin loss for deep face recognition"

Connected through hyperedge HE-5 (induced by source paper "disentangled representation learning for 3d face shape"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "alive caricature from 2d to 3d"
    - "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-6 (induced by source paper "gait recognition via disentangled representation learning"):
  with members:
    - "representation learning by rotating your faces"
    - "synthesizing images of humans in unseen poses"
    - "pedestrian detection with autoregressive network phases"
    - "nonlinear 3d face morphable model"
    - "unsupervised learning of disentangled representations from video"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "mask r cnn"
    - "rmpe regional multi person pose estimation"

Connected through hyperedge HE-7 (induced by source paper "towards interpretable face recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "representation learning by rotating your faces"
    - "on learning 3d face morphable model from in the wild images"
    - "gait recognition via disentangled representation learning"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "multicolumn networks for face recognition"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-8 (induced by source paper "robust face recognition with deeply normalized depth images"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "a lightened cnn for deep face representation"
    - "learning rich features from rgb d images for object detection and 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)