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 "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  Abstract: Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn with less/other types of supervision, have been proposed. We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research.

Includes members:
  Member "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weakly and semi supervised detection in medical imaging via deep dual branch net")
  Member "attention based deep multiple instance learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust attentional aggregation of deep feature sets for multi view 3d reconstruction")
  Member "unsupervised reverse domain adaptation for synthetic medical images via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "unsupervised anomaly detection with generative adversarial networks to guide marker discovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "multisource transfer learning with convolutional neural networks for lung pattern analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "deep learning with permutation invariant operator for multi instance histopathology classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "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")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the future of digital health with federated learning")
  Member "risk stratification of lung nodules using 3d cnn based multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gated dilated networks for lung nodule classification in ct scans")

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)