human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"
  Abstract: Digital holographic microscopy enables the 3D reconstruction of volumetric samples from a single-snapshot hologram. However, unlike a conventional bright-field microscopy image, the quality of holographic reconstructions is compromised by interference fringes as a result of twin images and out-of-plane objects. Here, we demonstrate that cross-modality deep learning using a generative adversarial network (GAN) can endow holographic images of a sample volume with bright-field microscopy contrast, combining the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of incoherent bright-field microscopy. We illustrate the performance of this “bright-field holography” method through the snapshot imaging of bioaerosols distributed in 3D, matching the artifact-free image contrast and axial sectioning performance of a high-NA bright-field microscope. This data-driven deep-learning-based imaging method bridges the contrast gap between coherent and incoherent imaging, and enables the snapshot 3D imaging of objects with bright-field contrast from a single hologram, benefiting from the wave-propagation framework of holography.

Connected through hyperedge HE-1 (induced by source paper "three dimensional virtual refocusing of fluorescence microscopy images using deep learning"):
  with members:
    - "resolution enhancement in scanning electron microscopy using deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "deep learning enhanced mobile phone microscopy"
    - "deep learning based super resolution in coherent imaging systems"
    - "isotropic reconstruction of 3d fluorescence microscopy images using convolutional neural networks"
    - "deep learning microscopy"
    - "diffusercam lensless single exposure 3d imaging"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

Connected through hyperedge HE-2 (induced by source paper "deep learning based super resolution in coherent imaging systems"):
  with members:
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)