human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation"):
  Abstract: Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Our framework can effectively leverage the unlabeled data by encouraging consistent predictions of the same input under different perturbations. Concretely, the framework consists of a student model and a teacher model, and the student model learns from the teacher model by minimizing a segmentation loss and a consistency loss with respect to the targets of the teacher model. We design a novel uncertainty-aware scheme to enable the student model to gradually learn from the meaningful and reliable targets by exploiting the uncertainty information. Experiments show that our method achieves high performance gains by incorporating the unlabeled data. Our method outperforms the state-of-the-art semi-supervised methods, demonstrating the potential of our framework for the challenging semi-supervised problems.

Includes members:
  Member "semi supervised brain lesion segmentation with an adapted mean teacher model":
    also connected through other hyperedges:
      (none)
  Member "factorised spatial representation learning application in semi supervised myocardial segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust multimodal brain tumor segmentation via feature disentanglement and gated fusion")
  Member "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "semi supervised multi organ segmentation via deep multi planar co training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels")
  Member "semi supervised skin lesion segmentation via transformation consistent self ensembling model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised medical image segmentation via learning consistency under transformations")
  Member "deep semi supervised segmentation with weight averaged consistency targets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistency regularization and cutmix for semi supervised semantic segmentation")
  Member "multi task learning for left atrial segmentation on ge mri":
    also connected through other hyperedges:
      (none)
  Member "semi supervised deep learning for fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep co training for semi supervised image segmentation")

Question: Please tell me which class the source paper 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)