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 "parallel medical imaging a new data knowledge driven evolutionary framework for medical image analysis"):
  Abstract: There has been much progress in data-driven artificial intelligence technology for medical image analysis in last decades. However, it still remains a challenge due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven evolutionary framework termed as Parallel Medical Imaging (PMI) for medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experiments with predictive learning in a data-driven way are conducted to extract medical knowledge for diagnostic decision support. Artificial imaging systems are introduced to select and prescriptively generate medical image data in a knowledge-driven way to utilize medical domain knowledge. Through the parallel evolutionary optimization, our proposed PMI framework can boost the generalization ability and alleviate the limitation of medical interpretation for diagnostic decision. A GANs-based PMI framework for case studies of mammogram analysis is demonstrated in this work.

Includes members:
  Member "melanogans high resolution skin lesion synthesis with gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dermgan synthetic generation of clinical skin images with pathology")
  Member "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "icadx interpretable computer aided diagnosis of breast masses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "iterative augmentation of visual evidence for weakly supervised lesion localization in deep interpretability frameworks")
  Member "gans for medical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks")
  Member "gan based synthetic medical image augmentation for increased cnn performance in liver lesion classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "opengan open set generative adversarial networks")
  Member "what do we need to build explainable ai systems for the medical domain":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "foundations of explainable knowledge enabled systems")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)