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: "review of statistical shape spaces for 3d data with comparative analysis for human faces"
  Abstract: Abstract   With systems for acquiring 3D surface data being evermore commonplace, it has become important to reliably extract specific shapes from the acquired data. In the presence of noise and occlusions, this can be done through the use of statistical shape models, which are learned from databases of clean examples of the shape in question. In this paper, we review, analyze and compare different statistical models: from those that analyze the variation in geometry globally to those that analyze the variation in geometry locally. We first review how different types of models have been used in the literature, then proceed to define the models and analyze them theoretically, in terms of both their statistical and computational aspects. We then perform extensive experimental comparison on the task of model fitting, and give intuition about which type of model is better for a few applications. Due to the wide availability of databases of high-quality data, we use the human face as the specific shape we wish to extract from corrupted data.

Connected through hyperedge HE-1 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "inverting the generator of a generative adversarial network ii"
    - "relational inductive biases deep learning and graph networks"
    - "escaping from collapsing modes in a constrained space"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "motifnet a motif based graph convolutional network for directed graphs"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"

Connected through hyperedge HE-2 (induced by source paper "3dfacegan adversarial nets for 3d face representation generation and translation"):
  with members:
    - "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"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "began boundary equilibrium generative adversarial networks"
    - "deriving neural architectures from sequence and graph kernels"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "synthesizing coupled 3d face modalities by trunk branch generative adversarial networks"):
  with members:
    - "meshgan non linear 3d morphable models of faces"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "representation learning by rotating your faces"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "expressive body capture 3d hands face and body from a single image"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "using a single rgb frame for real time 3d hand pose estimation in the wild"
    - "end to end recovery of human shape and pose"
    - "unite the people closing the loop between 3d and 2d human representations"
    - "dynamics based 3d skeletal hand tracking"

Connected through hyperedge HE-5 (induced by source paper "towards a complete 3d morphable model of the human head"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "towards high fidelity nonlinear 3d face morphable model"
    - "3d morphable face models past present and future"
    - "3dfacegan adversarial nets for 3d face representation generation and translation"
    - "combining 3d morphable models a large scale face and head model"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "head reconstruction from internet photos"
    - "total capture a 3d deformation model for tracking faces hands and bodies"

Connected through hyperedge HE-6 (induced by source paper "visibility constrained generative model for depth based 3d facial pose tracking"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "end to end 3d face reconstruction with deep neural networks"
    - "cnn based real time dense face reconstruction with inverse rendered photo realistic face images"
    - "3d face morphable models in the wild"
    - "capturing dynamic textured surfaces of moving targets"
    - "learning detailed face reconstruction from a single image"
    - "dense human body correspondences using convolutional networks"

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"
    - "morphable face models an open framework"

Connected through hyperedge HE-8 (induced by source paper "generating 3d faces using convolutional mesh autoencoders"):
  with members:
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "deformable shape completion with graph convolutional autoencoders"
    - "extreme 3d face reconstruction looking past occlusions"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "geometric deep learning going beyond euclidean data"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning shape correspondence with anisotropic convolutional neural networks"

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)