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 "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.

Includes members:
  Member "lensless computational imaging through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blending diverse physical priors with neural networks")
  Member "deep learning microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter")
  Member "deep learning for photoacoustic tomography from sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully dense unet for 2 d sparse photoacoustic tomography artifact removal")
  Member "deep convolutional neural network for inverse problems in imaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "π nets deep polynomial neural networks")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "image super resolution using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "phase retrieval via matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)