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: "factorised spatial representation learning application in semi supervised myocardial segmentation"
  Abstract: The success and generalisation of deep learning algorithms heavily depend on learning good feature representations. In medical imaging this entails representing anatomical information, as well as properties related to the specific imaging setting. Anatomical information is required to perform further analysis, whereas imaging information is key to disentangle scanner variability and potential artefacts. The ability to factorise these would allow for training algorithms only on the relevant information according to the task. To date, such factorisation has not been attempted. In this paper, we propose a methodology of latent space factorisation relying on the cycle-consistency principle. As an example application, we consider cardiac MR segmentation, where we separate information related to the myocardium from other features related to imaging and surrounding substructures. We demonstrate the proposed method's utility in a semi-supervised setting: we use very few labelled images together with many unlabelled images to train a myocardium segmentation neural network. Compared to fully supervised networks we achieve comparable performance using a fraction of labelled images in experiments on ACDC and a private data set.

Connected through hyperedge HE-1 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"):
  with members:
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "brain tumor segmentation on mri with missing modalities"
    - "multimodal unsupervised image to image translation"
    - "learning factorized multimodal representations"
    - "a deep learning model integrating fcnns and crfs for brain tumor segmentation"
    - "improving cnn training using disentanglement for liver lesion classification in ct"
    - "scalable multimodal convolutional networks for brain tumour segmentation"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"

Connected through hyperedge HE-2 (induced by source paper "disentangle align and fuse for multimodal and zero shot image segmentation"):
  with members:
    - "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion"
    - "theory and evaluation metrics for learning disentangled representations"
    - "transgaga geometry aware unsupervised image to image translation"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "unsupervised deformable registration for multi modal images via disentangled representations"
    - "semantic image synthesis with spatially adaptive normalization"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"
    - "translating and segmenting multimodal medical volumes with cycle and shape consistency generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "disentangled representation learning in cardiac image analysis"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "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"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation"):
  with members:
    - "semi supervised brain lesion segmentation with an adapted mean teacher model"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "multi task learning for left atrial segmentation on ge mri"
    - "semi supervised deep learning for fully convolutional networks"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"

Connected through hyperedge HE-5 (induced by source paper "robust image segmentation quality assessment without ground truth"):
  with members:
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "identifying and categorizing anomalies in retinal imaging data"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning by disentangling and self ensembling over stochastic latent space"):
  with members:
    - "improving generalization of deep networks for inverse reconstruction of image sequences"
    - "a survey on deep learning in medical image analysis"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-7 (induced by source paper "decompose to manipulate manipulable object synthesis in 3d medical images with structured image decomposition"):
  with members:
    - "on the convergence of adam and beyond"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "conditional infilling gans for data augmentation in mammogram classification"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "spectral normalization for generative adversarial networks"
    - "learning hierarchical semantic image manipulation through structured representations"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"

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)