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: "deforming autoencoders unsupervised disentangling of shape and appearance"
  Abstract: In this work we introduce Deforming Autoencoders, a generative model for images that disentangles shape from appearance in an unsupervised manner. As in the deformable template paradigm, shape is represented as a deformation between a canonical coordinate system (`template') and an observed image, while appearance is modeled in `canonical', template, coordinates, thus discarding variability due to deformations. We introduce novel techniques that allow this approach to be deployed in the setting of autoencoders and show that this method can be used for unsupervised group-wise image alignment. We show experiments with expression morphing in humans, hands, and digits, face manipulation, such as shape and appearance interpolation, as well as unsupervised landmark localization. A more powerful form of unsupervised disentangling becomes possible in template coordinates, allowing us to successfully decompose face images into shading and albedo, and further manipulate face images.

Connected through hyperedge HE-1 (induced by source paper "neural puppet generative layered cartoon characters"):
  with members:
    - "multi chart generative surface modeling"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "learning category specific mesh reconstruction from image collections"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "densepose dense human pose estimation in the wild"
    - "variational autoencoders for deforming 3d mesh models"
    - "deformable shape completion with graph convolutional autoencoders"
    - "octree generating networks efficient convolutional architectures for high resolution 3d outputs"

Connected through hyperedge HE-2 (induced by source paper "texture deformation based generative adversarial networks for face editing"):
  with members:
    - "multimodal unsupervised image to image translation"
    - "mask aware photorealistic face attribute manipulation"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "rsgan face swapping and editing using face and hair representation in latent spaces"
    - "dna gan learning disentangled representations from multi attribute images"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "neural face editing with intrinsic image disentangling"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "linear and deformable image registration with 3d convolutional neural networks"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "adversarial image registration with application for mr and trus image fusion"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "densepose dense human pose estimation in the wild"
    - "squeeze and excitation networks"
    - "deformable registration through learning of context specific metric aggregation"
    - "a deep metric for multimodal registration"
    - "spatial transformer networks"

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)