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: "disentangling factors of variation by mixing them"
  Abstract: We propose an approach to learn image representations that consist of disentangled factors of variation without exploiting any manual labeling or data domain knowledge. A factor of variation corresponds to an image attribute that can be discerned consistently across a set of images, such as the pose or color of objects. Our disentangled representation consists of a concatenation of feature chunks, each chunk representing a factor of variation. It supports applications such as transferring attributes from one image to another, by simply mixing and unmixing feature chunks, and classification or retrieval based on one or several attributes, by considering a user-specified subset of feature chunks. We learn our representation without any labeling or knowledge of the data domain, using an autoencoder architecture with two novel training objectives: first, we propose an invariance objective to encourage that encoding of each attribute, and decoding of each chunk, are invariant to changes in other attributes and chunks, respectively; second, we include a classification objective, which ensures that each chunk corresponds to a consistently discernible attribute in the represented image, hence avoiding degenerate feature mappings where some chunks are completely ignored. We demonstrate the effectiveness of our approach on the MNIST, Sprites, and CelebA datasets.

Connected through hyperedge HE-1 (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"
    - "unsupervised part based disentangling of object shape and appearance"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "mixnmatch multifactor disentanglement and encoding for conditional image generation"):
  with members:
    - "unsupervised part based disentangling of object shape and appearance"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "disentangling pose from appearance in monochrome hand images"):
  with members:
    - "synthesizing images of humans in unseen poses"
    - "disentangling latent hands for image synthesis and pose estimation"
    - "augmented skeleton space transfer for depth based hand pose estimation"
    - "exploring disentangled feature representation beyond face identification"
    - "image to image translation for cross domain disentanglement"
    - "vip cnn visual phrase guided convolutional neural network"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"

Connected through hyperedge HE-4 (induced by source paper "dualdis dual branch disentangling with adversarial learning"):
  with members:
    - "a two step disentanglement method"
    - "learning disentangled representations with reference based variational autoencoders"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "label propagation for deep semi supervised learning"
    - "dual swap disentangling"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"
    - "exploring disentangled feature representation beyond face identification"
    - "isolating sources of disentanglement in variational autoencoders"

Connected through hyperedge HE-5 (induced by source paper "product of orthogonal spheres parameterization for disentangled representation learning"):
  with members:
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "learning disentangled representations with reference based variational autoencoders"
    - "disentangling pose from appearance in monochrome hand images"
    - "geometry of deep generative models for disentangled representations"
    - "disentangled representation learning for non parallel text style transfer"
    - "mr gan manifold regularized generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "disentangling factors of variation with cycle consistent variational auto encoders"

Connected through hyperedge HE-6 (induced by source paper "elastic infogan unsupervised disentangled representation learning in imbalanced data"):
  with members:
    - "finegan unsupervised hierarchical disentanglement for fine grained object generation and discovery"
    - "deep continuous clustering"
    - "exploring the limits of weakly supervised pretraining"
    - "domain adaptation for semantic segmentation via class balanced self training"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "unsupervised learning of disentangled representations from video"
    - "class rectification hard mining for imbalanced deep learning"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "disentangled representation learning in cardiac image analysis"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "multimodal unsupervised image to image translation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "a variational u net for conditional appearance and shape generation"
    - "deep learning based instance segmentation in 3d biomedical images using weak annotation"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "scalable multimodal convolutional networks for brain tumour segmentation"

Connected through hyperedge HE-8 (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"
    - "3d guided fine grained face manipulation"
    - "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"

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)