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: "fitting a 3d morphable model to edges a comparison between hard and soft correspondences"
  Abstract: In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.

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"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

Connected through hyperedge HE-2 (induced by source paper "high accuracy face geometry capture using a smartphone video"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "detailed human avatars from monocular video"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "robust registration and geometry estimation from unstructured facial scans"

Connected through hyperedge HE-3 (induced by source paper "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"):
  with members:
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "arcface additive angular margin loss for deep face recognition"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "deep appearance models for face rendering"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "learning to regress 3d face shape and expression from an image without 3d supervision"):
  with members:
    - "expressive body capture 3d hands face and body from a single image"
    - "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"
    - "evaluation of dense 3d reconstruction from 2d face images in the wild"
    - "nonlinear 3d face morphable model"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"

Connected through hyperedge HE-5 (induced by source paper "face manifold manifold learning for synthetic face generation"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "end to end 3d face reconstruction with deep neural networks"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "customized facial constant positive air pressure cpap masks"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "3d face reconstruction by learning from synthetic data"

Connected through hyperedge HE-6 (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-7 (induced by source paper "expnet landmark free deep 3d facial expressions"):
  with members:
    - "extreme 3d face reconstruction looking past occlusions"
    - "faceposenet making a case for landmark free face alignment"
    - "do we really need to collect millions of faces for effective face recognition"
    - "a multi scale cascade fully convolutional network face detector"
    - "pooling faces template based face recognition with pooled face images"
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"
    - "learning detailed face reconstruction from a single image"
    - "convolutional experts constrained local model for facial landmark detection"

Connected through hyperedge HE-8 (induced by source paper "3d face reconstruction with geometry details from a single image"):
  with members:
    - "regressing robust and discriminative 3d morphable models with a very deep neural network"

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)