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 "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  Abstract: Deep convolutional neural networks have achieved remarkable progress on a variety of medical image computing tasks. A common problem when applying supervised deep learning methods to medical images is the lack of labeled data, which is very expensive and time-consuming to be collected. In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data. To utilize the unlabeled data, our method encourages the consistent predictions of the network-in-training for the same input under different regularizations. Aiming for the semi-supervised segmentation problem, we enhance the effect of regularization for pixel-level predictions by introducing a transformation, including rotation and flipping, consistent scheme in our self-ensembling model. With the aim of semi-supervised segmentation tasks, we introduce a transformation consistent strategy in our self-ensembling model to enhance the regularization effect for pixel-level predictions. We have extensively validated the proposed semi-supervised method on three typical yet challenging medical image segmentation tasks: (i) skin lesion segmentation from dermoscopy images on International Skin Imaging Collaboration (ISIC) 2017 dataset, (ii) optic disc segmentation from fundus images on Retinal Fundus Glaucoma Challenge (REFUGE) dataset, and (iii) liver segmentation from volumetric CT scans on Liver Tumor Segmentation Challenge (LiTS) dataset. Compared to the state-of-the-arts, our proposed method shows superior segmentation performance on challenging 2D/3D medical images, demonstrating the effectiveness of our semi-supervised method for medical image segmentation.

Includes members:
  Member "the liver tumor segmentation benchmark lits":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on semi self and unsupervised techniques in image classification")
  Member "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "adversarial learning for semi supervised semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment")
  Member "unsupervised reverse domain adaptation for synthetic medical images via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")
  Member "isic 2017 skin lesion analysis towards melanoma detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks")
  Member "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "from patch to image segmentation using fully convolutional networks application to retinal images")

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)