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 "does deep learning always outperform simple linear regression in optical imaging"):
  Abstract: Deep learning has been extensively applied in many optical imaging applications in recent years. Despite the success, the limitations and drawbacks of deep learning in optical imaging have been seldom investigated. In this work, we show that conventional linear-regression-based methods can outperform the previously proposed deep learning approaches for two black-box optical imaging problems in some extent. Deep learning demonstrates its weakness especially when the number of training samples is small. The advantages and disadvantages of linear-regression-based methods and deep learning are analyzed and compared. Since many optical systems are essentially linear, a deep learning network containing many nonlinearity functions sometimes may not be the most suitable option.

Includes members:
  Member "review on optical image hiding and watermarking techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple image encryption and hiding with an optical diffractive neural network")
  Member "experimental comparison of single pixel imaging algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast object classification in single pixel imaging")
  Member "end to end deep learning of optical fiber communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback")
  Member "deep learning approach for fourier ptychography microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blending diverse physical priors with neural networks")
  Member "computational ghost imaging using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast object classification in single pixel imaging")
  Member "phase recovery and holographic image reconstruction using deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exascale deep learning for scientific inverse problems")
  Member "solving fourier ptychographic imaging problems via neural network modeling and tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantitative phase imaging and artificial intelligence a review")
  Member "lensless computational imaging through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blending diverse physical priors with neural networks")

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)