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 "feature2mass visual feature processing in latent space for realistic labeled mass generation"):
  Abstract: This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields including computer-aided detection and diagnosis. In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required. However, in many bioimaging fields, the large-size of labeled dataset is scarcely available. Although a few researches have been dedicated to solving this problem through generative model, there are some problems as follows: 1) The generated bio-image does not seem realistic; 2) the variation of generated bio-image is limited; and 3) additional label annotation task is needed. In this study, we propose a realistic labeled bio-image generation method through visual feature processing in latent space. Experimental results have shown that mass images generated by the proposed method were realistic and had wide expression range of targeted mass characteristics.

Includes members:
  Member "synthetic data augmentation using gan for improved liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "imperfect imaganation implications of gans exacerbating biases on facial data augmentation and snapchat selfie lenses")
  Member "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "opengan open set generative adversarial networks")
  Member "deep generative adversarial neural networks for realistic prostate lesion mri synthesis":
    also connected through other hyperedges:
      (none)
  Member "towards adversarial retinal image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ophthalmic diagnosis using deep learning with fundus images a critical review")
  Member "how to fool radiologists with generative adversarial networks a visual turing test for lung cancer diagnosis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "infinite brain mr images pggan based data augmentation for tumor detection")
  Member "virtual pet images from ct data using deep convolutional networks initial results":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ct gan malicious tampering of 3d medical imagery using deep learning")
  Member "medical image synthesis with context aware generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "improving computer aided detection using convolutional neural networks and random view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")

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)