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: "unrestricted facial geometry reconstruction using image to image translation"
  Abstract: It has been recently shown that neural networks can recover the geometric structure of a face from a single given image. A common denominator of most existing face geometry reconstruction methods is the restriction of the solution space to some low-dimensional subspace. While such a model significantly simplifies the reconstruction problem, it is inherently limited in its expressiveness. As an alternative, we propose an Image-to-Image translation network that jointly maps the input image to a depth image and a facial correspondence map. This explicit pixel-based mapping can then be utilized to provide high quality reconstructions of diverse faces under extreme expressions, using a purely geometric refinement process. In the spirit of recent approaches, the network is trained only with synthetic data, and is then evaluated on in-the-wild facial images. Both qualitative and quantitative analyses demonstrate the accuracy and the robustness of our approach.

Connected through hyperedge HE-1 (induced by source paper "the whole is greater than the sum of its nonrigid parts"):
  with members:
    - "amass archive of motion capture as surface shapes"
    - "4d spatio temporal convnets minkowski convolutional neural networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "meshcnn a network with an edge"
    - "correspondence free region localization for partial shape similarity via hamiltonian spectrum alignment"
    - "meteornet deep learning on dynamic 3d point cloud sequences"
    - "framenet learning local canonical frames of 3d surfaces from a single rgb image"
    - "deep hough voting for 3d object detection in point clouds"

Connected through hyperedge HE-2 (induced by source paper "audio driven talking face video generation with learning based personalized head pose"):
  with members:
    - "realistic speech driven facial animation with gans"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "ganfit generative adversarial network fitting for high fidelity 3d face reconstruction"
    - "neural voice puppetry audio driven facial reenactment"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "lip movements generation at a glance"

Connected through hyperedge HE-3 (induced by source paper "audio driven talking face video generation with natural head pose"):
  with members:
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "few shot adversarial learning of realistic neural talking head models"
    - "one shot face reenactment"
    - "accurate 3d face reconstruction with weakly supervised learning from single image to image set"
    - "speech2face learning the face behind a voice"
    - "video to video synthesis"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "talking face generation by adversarially disentangled audio visual representation"

Connected through hyperedge HE-4 (induced by source paper "high accuracy face geometry capture using a smartphone video"):
  with members:
    - "3d face reconstruction with geometry details from a single image"
    - "detailed human avatars from monocular video"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "mvsnet depth inference for unstructured multi view stereo"
    - "sparse photometric 3d face reconstruction guided by morphable models"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "robust registration and geometry estimation from unstructured facial scans"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "360 degree textures of people in clothing from a single image"):
  with members:
    - "tex2shape detailed full human body geometry from a single image"
    - "textured neural avatars"
    - "unsupervised part based disentangling of object shape and appearance"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "pifu pixel aligned implicit function for high resolution clothed human digitization"
    - "disn deep implicit surface network for high quality single view 3d reconstruction"
    - "pose guided human video generation"
    - "synthesizing images of humans in unseen poses"

Connected through hyperedge HE-6 (induced by source paper "tex2shape detailed full human body geometry from a single image"):
  with members:
    - "relighting humans occlusion aware inverse rendering for full body human images"
    - "learning to reconstruct people in clothing from a single rgb camera"
    - "deephuman 3d human reconstruction from a single image"
    - "simulcap single view human performance capture with cloth simulation"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bodynet volumetric inference of 3d human body shapes"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "fml face model learning from videos"

Connected through hyperedge HE-7 (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-8 (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"

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)