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: "inversefacenet deep single shot inverse face rendering from a single image"
  Abstract: We introduce InverseFaceNet, a deep convolutional inverse rendering framework for faces that jointly estimates facial pose, shape, expression, reflectance and illumination from a single input image in a single shot. By estimating all these parameters from just a single image, advanced editing possibilities on a single face image, such as appearance editing and relighting, become feasible. Previous learning-based face reconstruction approaches do not jointly recover all dimensions, or are severely limited in terms of visual quality. In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created dataset. Our approach builds on a novel loss function that measures model-space similarity directly in parameter space and significantly improves reconstruction accuracy. In addition, we propose an analysis-by-synthesis breeding approach which iteratively updates the synthetic training corpus based on the distribution of real-world images, and we demonstrate that this strategy outperforms completely synthetically trained networks. Finally, we show high-quality reconstructions and compare our approach to several state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "pushing the envelope for rgb based dense 3d hand pose estimation via neural rendering"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "analysis of hand segmentation in the wild"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deephps end to end estimation of 3d hand pose and shape by learning from synthetic depth"
    - "hand pose estimation via latent 2 5d heatmap regression"
    - "cross modal deep variational hand pose estimation"

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"
    - "disentangling features in 3d face shapes for joint face reconstruction and recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning perspective undistortion of portraits"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "what does 2d geometric information really tell us about 3d face shape"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "a perceptual measure for deep single image camera calibration"
    - "synthesizing normalized faces from facial identity features"
    - "towards large pose face frontalization in the wild"
    - "horizon lines in the wild"

Connected through hyperedge HE-4 (induced by source paper "biofacenet deep biophysical face image interpretation"):
  with members:
    - "decomposing multispectral face images into diffuse and specular shading and biophysical parameters"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-5 (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-6 (induced by source paper "priming deep neural networks with synthetic faces for enhanced performance"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "can synthetic faces undo the damage of dataset bias to face recognition and facial landmark detection"):
  with members:
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "contemplating visual emotions understanding and overcoming dataset bias"
    - "what are the biases in my word embedding"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "empirically analyzing the effect of dataset biases on deep face recognition systems"
    - "unrealcv connecting computer vision to unreal engine"

Connected through hyperedge HE-8 (induced by source paper "a hybrid model for identity obfuscation by face replacement"):
  with members:
    - "person recognition in personal photo collections"
    - "end to end 3d face reconstruction with deep neural networks"
    - "a domain based approach to social relation recognition"
    - "semi adversarial networks convolutional autoencoders for imparting privacy to face images"
    - "unrestricted facial geometry reconstruction using image to image translation"
    - "natural and effective obfuscation by head inpainting"
    - "3d face morphable models in the wild"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"

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)