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: "phase recovery and holographic image reconstruction using deep learning in neural networks"
  Abstract: A new method that uses neural-network-based deep learning could lead to faster and more accurate holographic image reconstruction and phase recovery. Optoelectronic sensors such as charge-coupled devices and complementary metal-oxide–semiconductor imagers are sensitive to intensity but are unable to directly detect the phase of light waves diffracted from an object. Additional information or measurement is thus needed to recover the missing phase information, which enables reconstructing the image of the sample. Now, Aydogan Ozcan and colleagues from the University of California, Los Angeles in the USA have designed a neural network that can perform phase recovery and holographic image reconstruction from a single intensity-only hologram. Using deep learning, they demonstrated the elimination of twin-image and self-interference-related spatial artifacts arising from missing phase information. The technique could significantly simplify the imaging hardware and speed up the image acquisition and reconstruction processes in various holographic and coherent imaging systems.

Connected through hyperedge HE-1 (induced by source paper "exascale deep learning for scientific inverse problems"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "energy and policy considerations for deep learning in nlp"
    - "beyond data and model parallelism for deep neural networks"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "revisiting unreasonable effectiveness of data in deep learning era"

Connected through hyperedge HE-2 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "all optical machine learning using diffractive deep neural networks"
    - "deep learning enhanced mobile phone microscopy"
    - "principles of neuromorphic photonics"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-3 (induced by source paper "does deep learning always outperform simple linear regression in optical imaging"):
  with members:
    - "review on optical image hiding and watermarking techniques"
    - "experimental comparison of single pixel imaging algorithms"
    - "end to end deep learning of optical fiber communications"
    - "deep learning approach for fourier ptychography microscopy"
    - "computational ghost imaging using deep learning"
    - "solving fourier ptychographic imaging problems via neural network modeling and tensorflow"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-4 (induced by source paper "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"):
  with members:
    - "deep learning enhanced mobile phone microscopy"
    - "deep learning microscopy"
    - "identity mappings in deep residual networks"
    - "image to image translation with conditional adversarial networks"
    - "context encoders feature learning by inpainting"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "bright field holography cross modality deep learning enables snapshot 3d imaging with bright field contrast using a single hologram"):
  with members:
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "lensless computational imaging through deep learning"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "robust x ray sparse view phase tomography via hierarchical synthesis convolutional neural networks"):
  with members:
    - "non local low rank cube based tensor factorization for spectral ct reconstruction"
    - "learning dual convolutional neural networks for low level vision"
    - "learning to synthesize splitting and recombining low and high spatial frequencies for image recovery"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "quantitative phase imaging and artificial intelligence a review"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "extended depth of field in holographic imaging using deep learning based autofocusing and phase recovery"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "choicenet robust learning by revealing output correlations"
    - "deep learning enhanced mobile phone microscopy"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning to synthesize splitting and recombining low and high spatial frequencies for image recovery"):
  with members:
    - "learning dual convolutional neural networks for low level vision"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "understanding neural networks through deep visualization"

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)