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: "morphable face models an open framework"
  Abstract: In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.

Connected through hyperedge HE-1 (induced by source paper "rank3dgan semantic mesh generation using relative attributes"):
  with members:
    - "multi chart generative surface modeling"
    - "learning category specific mesh reconstruction from image collections"
    - "ranking cgans subjective control over semantic image attributes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "sparse data driven mesh deformation"
    - "variational autoencoders for deforming 3d mesh models"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"

Connected through hyperedge HE-2 (induced by source paper "face to parameter translation for game character auto creation"):
  with members:
    - "unsupervised training for 3d morphable model regression"
    - "arbitrary style transfer with deep feature reshuffle"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "end to end 3d face reconstruction with deep neural networks"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "extreme 3d face reconstruction seeing through occlusions"

Connected through hyperedge HE-3 (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-4 (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"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "3d coded 3d correspondences by deep deformation"
    - "dense 3d face correspondence"

Connected through hyperedge HE-5 (induced by source paper "photo realistic facial details synthesis from single image"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised training for 3d morphable model regression"
    - "3d face reconstruction with geometry details from a single image"
    - "expnet landmark free deep 3d facial expressions"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "real time convolutional neural networks for emotion and gender classification"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "a closest point proposal for mcmc based probabilistic surface registration"):
  with members:
    - "informed mcmc with bayesian neural networks for facial image analysis"
    - "iterative global similarity points a robust coarse to fine integration solution for pairwise 3d point cloud registration"
    - "the informed sampler"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "combining 3d morphable models a large scale face and head model"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "head reconstruction from internet photos"
    - "3d face morphable models in the wild"
    - "review of statistical shape spaces for 3d data with comparative analysis for human faces"

Connected through hyperedge HE-8 (induced by source paper "real time facial surface geometry from monocular video on mobile gpus"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

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)