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 "med3d transfer learning for 3d medical image analysis"):
  Abstract: The performance on deep learning is significantly affected by volume of training data. Models pre-trained from massive dataset such as ImageNet become a powerful weapon for speeding up training convergence and improving accuracy. Similarly, models based on large dataset are important for the development of deep learning in 3D medical images. However, it is extremely challenging to build a sufficiently large dataset due to difficulty of data acquisition and annotation in 3D medical imaging. We aggregate the dataset from several medical challenges to build 3DSeg-8 dataset with diverse modalities, target organs, and pathologies. To extract general medical three-dimension (3D) features, we design a heterogeneous 3D network called Med3D to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models. We transfer Med3D pre-trained models to lung segmentation in LIDC dataset, pulmonary nodule classification in LIDC dataset and liver segmentation on LiTS challenge. Experiments show that the Med3D can accelerate the training convergence speed of target 3D medical tasks 2 times compared with model pre-trained on Kinetics dataset, and 10 times compared with training from scratch as well as improve accuracy ranging from 3% to 20%. Transferring our Med3D model on state-the-of-art DenseASPP segmentation network, in case of single model, we achieve 94.6\% Dice coefficient which approaches the result of top-ranged algorithms on the LiTS challenge.

Includes members:
  Member "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "e uda efficient unsupervised domain adaptation for cross site medical image segmentation")
  Member "rethinking imagenet pre training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rethinking the hyperparameters for fine tuning")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "mr based synthetic ct generation using a deep convolutional neural network method":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "understanding the mechanisms of deep transfer learning for medical images":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")

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)