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: "learning 3d deformation of animals from 2d images"
  Abstract: Understanding how an animal can deform and articulate is essential for a realistic modification of its 3D model. In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal. We present a volumetric deformation framework that produces a set of new 3D models by deforming a template 3D model according to a set of user-clicked images. Our framework is based on a novel locally-bounded deformation energy, where every local region has its own stiffness value that bounds how much distortion is allowed at that location. We jointly learn the local stiffness bounds as we deform the template 3D mesh to match each user-clicked image. We show that this seemingly complex task can be solved as a sequence of convex optimization problems. We demonstrate the effectiveness of our approach on cats and horses, which are highly deformable and articulated animals. Our framework produces new 3D models of animals that are significantly more plausible than methods without learned stiffness.

Connected through hyperedge HE-1 (induced by source paper "3dn 3d deformation network"):
  with members:
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "learning free form deformations for 3d object reconstruction"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-2 (induced by source paper "composite shape modeling via latent space factorization"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "learning part generation and assembly for structure aware shape synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "gibson env real world perception for embodied agents"
    - "semantic structure and interpretability of word embeddings"
    - "projecting embeddings for domain adaptation joint modeling of sentiment analysis in diverse domains"
    - "ganimation anatomically aware facial animation from a single image"
    - "structure aware generative network for 3d shape modeling"

Connected through hyperedge HE-3 (induced by source paper "three d safari learning to estimate zebra pose shape and texture from images in the wild"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "creatures great and smal recovering the shape and motion of animals from video"
    - "3d pose regression using convolutional neural networks"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-4 (induced by source paper "animals in virtual environments"):
  with members:
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "3d menagerie modeling the 3d shape and pose of animals"

Connected through hyperedge HE-5 (induced by source paper "learning and tracking the 3d body shape of freely moving infants from rgb d sequences"):
  with members:
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "detailed human avatars from monocular video"
    - "human motion analysis with deep metric learning"
    - "doublefusion real time capture of human performances with inner body shapes from a single depth sensor"
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "building statistical shape spaces for 3d human modeling"

Connected through hyperedge HE-6 (induced by source paper "3d menagerie modeling the 3d shape and pose of animals"):
  with members:
    - "unite the people closing the loop between 3d and 2d human representations"
    - "behavior discovery and alignment of articulated object classes from unstructured video"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "category specific object reconstruction from a single image"

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)