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 "unsupervised domain adaptation for medical imaging segmentation with self ensembling"):
  Abstract: Recent advances in deep learning methods have come to define the state-of-the-art for many medical imaging applications, surpassing even human judgment in several tasks. Those models, however, when trained to reduce the empirical risk on a single domain, fail to generalize when applied to other domains, a very common scenario in medical imaging due to the variability of images and anatomical structures, even across the same imaging modality. In this work, we extend the method of unsupervised domain adaptation using self-ensembling for the semantic segmentation task and explore multiple facets of the method on a small and realistic publicly-available magnetic resonance (MRI) dataset. Through an extensive evaluation, we show that self-ensembling can indeed improve the generalization of the models even when using a small amount of unlabelled data.

Includes members:
  Member "task driven generative modeling for unsupervised domain adaptation application to x ray image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one to one mapping for unpaired image to image translation")
  Member "automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "group normalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "semantic aware generative adversarial nets for unsupervised domain adaptation in chest x ray segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on direct distribution matching for adapting segmentation networks")
  Member "taskonomy disentangling task transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "deep learning in medical image analysis and multimodal learning for clinical decision support 4th international workshop dlmia 2018 and 8th international workshop ml cds 2018 held in conjunction with miccai 2018 granada spain september 20 2018 proceedings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional generative adversarial networks for data augmentation and adaptation in remotely sensed imagery")
  Member "dida disentangled synthesis for domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "emerging disentanglement in auto encoder based unsupervised image content transfer")
  Member "deep semi supervised segmentation with weight averaged consistency targets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "consistency regularization and cutmix for semi supervised semantic 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)