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 "semi supervised medical image segmentation via learning consistency under transformations"):
  Abstract: The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervised techniques that learn from a mixture of labeled and unlabeled images. In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images. More specifically, in this work we explore learning equivariance to elastic deformations. We implement this through: (1) a Siamese architecture with two identical branches, each of which receives a differently transformed image, and (2) a composite loss function with a supervised segmentation loss term and an unsupervised term that encourages segmentation consistency between the predictions of the two branches. We evaluate the method on a public dataset of chest radiographs with segmentations of anatomical structures using 5-fold cross-validation. The proposed method reaches significantly higher segmentation accuracy compared to supervised learning. This is due to learning transformation consistency on both labeled and unlabeled images, with the latter contributing the most. We achieve the performance comparable to state-of-the-art chest X-ray segmentation methods while using substantially fewer labeled images.

Includes members:
  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-1 (induced by source paper "a survey on semi self and unsupervised techniques in image classification")
  Member "semi supervised skin lesion segmentation via transformation consistent self ensembling model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "uncertainty aware self ensembling model for semi supervised 3d left atrium segmentation")
  Member "improving the segmentation of anatomical structures in chest radiographs using u net with an imagenet pre trained encoder":
    also connected through other hyperedges:
      (none)
  Member "deep semi supervised segmentation with weight averaged consistency targets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistency regularization and cutmix for semi supervised semantic segmentation")
  Member "semi supervised deep learning for fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep co training for semi supervised image segmentation")
  Member "improving landmark localization with semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised single view 3d reconstruction via semantic consistency")
  Member "exploiting the potential of unlabeled endoscopic video data with self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "regularization with stochastic transformations and perturbations for deep semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")

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)