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: "challenges in disentangling independent factors of variation"
  Abstract: We study the problem of building models that disentangle independent factors of variation. Such models could be used to encode features that can efficiently be used for classification and to transfer attributes between different images in image synthesis. As data we use a weakly labeled training set. Our weak labels indicate what single factor has changed between two data samples, although the relative value of the change is unknown. This labeling is of particular interest as it may be readily available without annotation costs. To make use of weak labels we introduce an autoencoder model and train it through constraints on image pairs and triplets. We formally prove that without additional knowledge there is no guarantee that two images with the same factor of variation will be mapped to the same feature. We call this issue the reference ambiguity. Moreover, we show the role of the feature dimensionality and adversarial training. We demonstrate experimentally that the proposed model can successfully transfer attributes on several datasets, but show also cases when the reference ambiguity occurs.

Connected through hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "diversity in faces"
    - "toward gender inclusive coreference resolution"
    - "robust ordinal vae employing noisy pairwise comparisons for disentanglement"
    - "weakly supervised disentanglement with guarantees"
    - "contrastive variational autoencoder enhances salient features"
    - "weakly supervised disentanglement by pairwise similarities"

Connected through hyperedge HE-2 (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-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 "towards disentangled representations for human retargeting by multi view learning"):
  with members:
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "a variational u net for conditional appearance and shape generation"
    - "deep appearance models for face rendering"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"

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 "demystifying inter class disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "generating diverse high fidelity images with vq vae 2"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "nam non adversarial unsupervised domain mapping"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

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)