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 morphable models as spatial transformer networks"
  Abstract: In this paper, we show how a 3D Morphable Model (i.e. a statistical model of the 3D shape of a class of objects such as faces) can be used to spatially transform input data as a module (a 3DMM-STN) within a convolutional neural network. This is an extension of the original spatial transformer network in that we are able to interpret and normalise 3D pose changes and self-occlusions. The trained localisation part of the network is independently useful since it learns to fit a 3D morphable model to a single image. We show that the localiser can be trained using only simple geometric loss functions on a relatively small dataset yet is able to perform robust normalisation on highly uncontrolled images including occlusion, self-occlusion and large pose changes.

Connected through hyperedge HE-1 (induced by source paper "synthesizing facial photometries and corresponding geometries using generative adversarial networks"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "semi supervised adversarial learning to generate photorealistic face images of new identities from 3d morphable model"
    - "densereg fully convolutional dense shape regression in the wild"
    - "efficient inter geodesic distance computation and fast classical scaling"
    - "high quality facial surface and texture synthesis via generative adversarial networks"
    - "nonlinear 3d face morphable model"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "unsupervised geometry aware representation for 3d human pose estimation"):
  with members:
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "learning monocular 3d human pose estimation from multi view images"
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "adversarial inverse graphics networks learning 2d to 3d lifting and image to image translation from unpaired supervision"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "unsupervised domain adaptation for 3d keypoint estimation via view consistency"
    - "learning a multi view stereo machine"
    - "lifting from the deep convolutional 3d pose estimation from a single image"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning of a facial attribute embedding from video"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "unsupervised discovery of object landmarks as structural representations"
    - "learnable pins cross modal embeddings for person identity"
    - "unsupervised representation learning by predicting image rotations"
    - "voxceleb2 deep speaker recognition"

Connected through hyperedge HE-4 (induced by source paper "joint 3d face reconstruction and dense alignment with position map regression network"):
  with members:
    - "multi attribute robust component analysis for facial uv maps"
    - "side information for face completion a robust pca approach"
    - "densereg fully convolutional dense shape regression in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "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"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-5 (induced by source paper "statistical transformer networks learning shape and appearance models via self supervision"):
  with members:
    - "unsupervised learning of object frames by dense equivariant image labelling"
    - "convolutional neural network architecture for geometric matching"
    - "deformable convolutional networks"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "toward end to end face recognition through alignment learning"
    - "dense transformer networks"
    - "faceposenet making a case for landmark free face alignment"
    - "unsupervised learning of object landmarks by factorized spatial embeddings"

Connected through hyperedge HE-6 (induced by source paper "convolutional point set representation a convolutional bridge between a densely annotated image and 3d face alignment"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "densepose dense human pose estimation in the wild"
    - "faster than real time facial alignment a 3d spatial transformer network approach in unconstrained poses"
    - "generative face completion"
    - "learning dense facial correspondences in unconstrained images"
    - "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"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"

Connected through hyperedge HE-7 (induced by source paper "x2face a network for controlling face generation by using images audio and pose codes"):
  with members:
    - "load balanced gans for multi view face image synthesis"
    - "geometry contrastive gan for facial expression transfer"
    - "extreme 3d face reconstruction seeing through occlusions"
    - "interpretable transformations with encoder decoder networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "kepler keypoint and pose estimation of unconstrained faces by learning efficient h cnn regressors"
    - "image to image translation with conditional adversarial networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "self supervised multi level face model learning for monocular reconstruction at over 250 hz"):
  with members:
    - "densereg fully convolutional dense shape regression in the wild"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "real time 3d face fitting and texture fusion on in the wild videos"
    - "inversefacenet deep single shot inverse face rendering from a single image"
    - "face alignment in the wild a survey"
    - "facial performance capture with deep 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)