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: "unsupervised depth estimation 3d face rotation and replacement"
  Abstract: We present an unsupervised approach for learning to estimate three dimensional (3D) facial structure from a single image while also predicting 3D viewpoint transformations that match a desired pose and facial geometry. We achieve this by inferring the depth of facial key-points in an input image in an unsupervised way. We show how it is possible to use these depths as intermediate computations within a new backpropable loss to predict the parameters of a 3D affine transformation matrix that maps inferred 3D key-points of an input face to corresponding 2D key-points on a desired target facial geometry or pose. Our resulting approach can therefore be used to infer plausible 3D transformations from one face pose to another, allowing faces to be frontalized, trans- formed into 3D models or even warped to another pose and facial geometry. Lastly, we identify certain shortcomings with our formulation, and explore adversarial image translation techniques as a post-processing step. Correspondingly, we explore several adversarial image transformation methods which allow us to re-synthesize complete head shots for faces re-targeted to different poses as well as repair images resulting from face replacements across identities.

Connected through hyperedge HE-1 (induced by source paper "rotate and render unsupervised photorealistic face rotation from single view images"):
  with members:
    - "maskgan towards diverse and interactive facial image manipulation"
    - "representation learning by rotating your faces"
    - "learning correspondence from the cycle consistency of time"
    - "semantic image synthesis with spatially adaptive normalization"
    - "face alignment in full pose range a 3d total solution"
    - "make a face towards arbitrary high fidelity face manipulation"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "vision infused deep audio inpainting"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning of probably symmetric deformable 3d objects from images in the wild"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "unsupervised generative 3d shape learning from natural images"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "unsupervised 3d pose estimation with geometric self supervision"

Connected through hyperedge HE-3 (induced by source paper "photo geometric autoencoding to learn 3d objects from unlabelled images"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "unsupervised 3d pose estimation with geometric self supervision"
    - "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"
    - "an adversarial neuro tensorial approach for learning disentangled representations"
    - "unsupervised 3d shape learning from image collections in the wild"
    - "learning view priors for single view 3d reconstruction"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised video depth estimation based on ego motion and disparity consensus"):
  with members:
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised learning of depth and ego motion from video"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

Connected through hyperedge HE-5 (induced by source paper "m2fpa a multi yaw multi pitch high quality database and benchmark for facial pose analysis"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "a style based generator architecture for generative adversarial networks"
    - "learning a high fidelity pose invariant model for high resolution face frontalization"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "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"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "towards large pose face frontalization in the wild"

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)