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 "deep learning based super resolution in coherent imaging systems"):
  Abstract: We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-limited coherent imaging systems. We experimentally validated the capabilities of this deep learning-based coherent imaging approach by super-resolving complex images acquired using a lensfree on-chip holographic microscope, the resolution of which was pixel size-limited. Using the same GAN-based approach, we also improved the resolution of a lens-based holographic imaging system that was limited in resolution by the numerical aperture of its objective lens. This deep learning-based super-resolution framework can be broadly applied to enhance the space-bandwidth product of coherent imaging systems using image data and convolutional neural networks, and provides a rapid, non-iterative method for solving inverse image reconstruction or enhancement problems in optics.

Includes members:
  Member "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning")
  Member "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning")
  Member "phase recovery and holographic image reconstruction using deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exascale deep learning for scientific inverse problems")
  Member "deep learning enhanced mobile phone microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning")
  Member "lensless computational imaging through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blending diverse physical priors with neural networks")
  Member "deep learning microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)