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 part based disentangling of object shape and appearance"
  Abstract: Large intra-class variation is the result of changes in multiple object characteristics. Images, however, only show the superposition of different variable factors such as appearance or shape. Therefore, learning to disentangle and represent these different characteristics poses a great challenge, especially in the unsupervised case. Moreover, large object articulation calls for a flexible part-based model. We present an unsupervised approach for disentangling appearance and shape by learning parts consistently over all instances of a category. Our model for learning an object representation is trained by simultaneously exploiting invariance and equivariance constraints between synthetically transformed images. Since no part annotation or prior information on an object class is required, the approach is applicable to arbitrary classes. We evaluate our approach on a wide range of object categories and diverse tasks including pose prediction, disentangled image synthesis, and video-to-video translation. The approach outperforms the state-of-the-art on unsupervised keypoint prediction and compares favorably even against supervised approaches on the task of shape and appearance transfer.

Connected through hyperedge HE-1 (induced by source paper "chained representation cycling learning to estimate 3d human pose and shape by cycling between representations"):
  with members:
    - "unsupervised learning of object structure and dynamics from videos"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "ordinal depth supervision for 3d human pose estimation"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "bodynet volumetric inference of 3d human body shapes"
    - "unsupervised adversarial learning of 3d human pose from 2d joint locations"

Connected through hyperedge HE-2 (induced by source paper "transferring dense pose to proximal animal classes"):
  with members:
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "deep learning tools for the measurement of animal behavior in neuroscience"
    - "billion scale semi supervised learning for image classification"
    - "three d safari learning to estimate zebra pose shape and texture from images in the wild"
    - "slim densepose thrifty learning from sparse annotations and motion cues"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "unsupervised learning of landmarks by descriptor vector exchange"
    - "cross domain adaptation for animal pose estimation"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of intrinsic structural representation points"):
  with members:
    - "cosegnet deep co segmentation of 3d shapes with group consistency loss"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "learning elementary structures for 3d shape generation and matching"
    - "supervised fitting of geometric primitives to 3d point clouds"
    - "bae net branched autoencoder for shape co segmentation"
    - "3d point cloud generative adversarial network based on tree structured graph convolutions"
    - "3d point capsule networks"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"

Connected through hyperedge HE-4 (induced by source paper "unsupervised disentanglement of pose appearance and background from images and videos"):
  with members:
    - "neural scene decomposition for multi person motion capture"
    - "semantic image synthesis with spatially adaptive normalization"
    - "video interpolation and prediction with unsupervised landmarks"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "synthesizing images of humans in unseen poses"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "stochastic adversarial video prediction"

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"
    - "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"
    - "coordinate based texture inpainting for pose guided image generation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised robust disentangling of latent characteristics for image synthesis"):
  with members:
    - "a two step disentanglement method"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "synthesizing images of humans in unseen poses"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "multimodal unsupervised image to image translation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "mine mutual information neural estimation"
    - "cross modal deep variational hand pose estimation"

Connected through hyperedge HE-7 (induced by source paper "mixnmatch multifactor disentanglement and encoding for conditional image generation"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "synthesizing images of humans in unseen poses"
    - "deformable generator network unsupervised disentanglement of appearance and geometry"
    - "multimodal unsupervised image to image translation"
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "a variational u net for conditional appearance and shape generation"

Connected through hyperedge HE-8 (induced by source paper "end to end learning of geometric deformations of feature maps for virtual try on"):
  with members:
    - "towards multi pose guided virtual try on network"
    - "synthesizing images of humans in unseen poses"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "the relativistic discriminator a key element missing from standard gan"
    - "look into person joint body parsing pose estimation network and a new benchmark"
    - "toward characteristic preserving image based virtual try on network"
    - "a variational u net for conditional appearance and shape generation"
    - "spatial fusion gan for image synthesis"

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)