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 guided fine grained face manipulation"
  Abstract: We present a method for fine-grained face manipulation. Given a face image with an arbitrary expression, our method can synthesize another arbitrary expression by the same person. This is achieved by first fitting a 3D face model and then disentangling the face into a texture and a shape. We then learn different networks in these two spaces. In the texture space, we use a conditional generative network to change the appearance, and carefully design input formats and loss functions to achieve the best results. In the shape space, we use a fully connected network to predict the accurate shapes and use the available depth data for supervision. Both networks are conditioned on expression coefficients rather than discrete labels, allowing us to generate an unlimited amount of expressions. We show the superiority of this disentangling approach through both quantitative and qualitative studies. In a user study, our method is preferred in 85% of cases when compared to the most recent work. When compared to the ground truth, annotators cannot reliably distinguish between our synthesized images and real images, preferring our method in 53% of the cases.

Connected through hyperedge HE-1 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"
    - "synthesizing images of humans in unseen poses"

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 "gesture to gesture translation in the wild via category independent conditional maps"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "pros and cons of gan evaluation measures"
    - "triple consistency loss for pairing distributions in gan based face synthesis"
    - "instagan instance aware image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "animating arbitrary objects via deep motion transfer"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-4 (induced by source paper "laplace landmark localization"):
  with members:
    - "ev action electromyography vision multi modal action dataset"
    - "video to video synthesis"
    - "red net a recurrent encoder decoder network for video based face alignment"
    - "densereg fully convolutional dense shape regression in the wild"
    - "3d human pose estimation in the wild by adversarial learning"
    - "animating arbitrary objects via deep motion transfer"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised generative 3d shape learning from natural images"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "learning view priors for single view 3d reconstruction"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "unsupervised training for 3d morphable model regression"
    - "a style based generator architecture for generative adversarial networks"
    - "escaping plato s cave using adversarial training 3d shape from unstructured 2d image collections"

Connected through hyperedge HE-6 (induced by source paper "digital twin acquiring high fidelity 3d avatar from a single image"):
  with members:
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "disentangled representation learning for 3d face shape"
    - "learning to regress 3d face shape and expression from an image without 3d supervision"
    - "alive caricature from 2d to 3d"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "nonlinear 3d face morphable model"
    - "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-7 (induced by source paper "animating arbitrary objects via deep motion transfer"):
  with members:
    - "multi channel attention selection gan with cascaded semantic guidance for cross view image translation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "video to video synthesis"
    - "synthesizing images of humans in unseen poses"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "recycle gan unsupervised video retargeting"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "every smile is unique landmark guided diverse smile generation"

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)