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 "efficient active learning for image classification and segmentation using a sample selection and conditional generative adversarial network"):
  Abstract: Training robust deep learning (DL) systems for medical image classification or segmentation is challenging due to limited images covering different disease types and severity. We propose an active learning (AL) framework to select most informative samples and add to the training data. We use conditional generative adversarial networks (cGANs) to generate realistic chest xray images with different disease characteristics by conditioning its generation on a real image sample. Informative samples to add to the training set are identified using a Bayesian neural network. Experiments show our proposed AL framework is able to achieve state of the art performance by using about 35% of the full dataset, thus saving significant time and effort over conventional methods.

Includes members:
  Member "night time haze and glow removal using deep dilated convolutional network":
    also connected through other hyperedges:
      (none)
  Member "informative sample generation using class aware generative adversarial networks for classification of chest xrays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "amd severity prediction and explainability using image registration and deep embedded clustering")
  Member "progressive generative adversarial networks for medical image super resolution":
    also connected through other hyperedges:
      (none)
  Member "combining transfer learning and segmentation information with gans for training data independent image registration":
    also connected through other hyperedges:
      (none)
  Member "amd severity prediction and explainability using image registration and deep embedded clustering":
    also connected through other hyperedges:
      (none)
  Member "training data independent image registration with gans using transfer learning and segmentation information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "informative sample generation using class aware generative adversarial networks for classification of chest xrays")
  Member "szoom a framework for automatic zoom into high resolution surveillance videos":
    also connected through other hyperedges:
      (none)
  Member "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization")

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)