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: "semi supervised multi organ segmentation via deep multi planar co training"
  Abstract: Multi-organ segmentation is a critical problem in medical image analysis due to its great value for computer-aided diagnosis, computer-aided surgery, and radiation therapy. Although fully-supervised segmentation methods can achieve good performance, they usually require a large amount of 3D data, such as CT scans, with voxel-wised annotations which are usually difficult, expensive, and slow to obtain. By contrast, large unannotated datasets of CT images are available. Inspired by the well-known semi-supervised learning framework co-training, we propose multi-planar co-training (MPCT), to generate more reliable pseudo-labels by enforcing consistency among multiple planes, i.e., saggital, coronal, and axial planes, of 3D unlabeled medical data, which play a vital role in our framework. Empirical results show that generating pseudo-labels by the multi-planar fusion rather than a single plane leads to a significant performance gain. We evaluate our approach on a new collected dataset and show that MPCT boosts the performance of a typical segmentation model, fully convolutional networks, by a large margin, when only a small set of labeled 3D data is available, i.e., 77.49% vs. 73.14%.

Connected through hyperedge HE-1 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"
    - "multi label image recognition with graph convolutional networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "exploring the limits of weakly supervised pretraining"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"

Connected through hyperedge HE-2 (induced by source paper "deep co training for semi supervised image segmentation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "deeply supervised salient object detection with short connections"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"
    - "deep co training for semi supervised image recognition"
    - "adversarial learning for semi supervised semantic segmentation"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-3 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"

Connected through hyperedge HE-4 (induced by source paper "constrained cnn losses for weakly supervised segmentation"):
  with members:
    - "constrained deep learning using conditional gradient and applications in computer vision"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "on regularized losses for weakly supervised cnn segmentation"
    - "imposing hard constraints on deep networks promises and limitations"
    - "learning random walk label propagation for weakly supervised semantic segmentation"
    - "curriculum domain adaptation for semantic segmentation of urban scenes"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "enet a deep neural network architecture for real time semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "revisiting cyclegan for semi supervised segmentation"):
  with members:
    - "deep co training for semi supervised image segmentation"
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised domain adaptation for medical imaging segmentation with self ensembling"
    - "semi supervised deep learning for fully convolutional networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "adversarial discriminative domain adaptation"

Connected through hyperedge HE-6 (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"
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "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-7 (induced by source paper "multi task attention based semi supervised learning for medical image segmentation"):
  with members:
    - "3d mri brain tumor segmentation using autoencoder regularization"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention gated networks learning to leverage salient regions in medical images"
    - "fully convolutional network ensembles for white matter hyperintensities segmentation in mr images"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-8 (induced by source paper "3d semi supervised learning with uncertainty aware multi view co training"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "deep co training for semi supervised image recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "3d anisotropic hybrid network transferring convolutional features from 2d images to 3d anisotropic volumes"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic image 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)