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: "review on optical image hiding and watermarking techniques"
  Abstract: Abstract   Information security is a critical issue in modern society and image watermarking can effectively prevent unauthorized information access. Optical image watermarking techniques generally have advantages of parallel high-speed processing and multi-dimensional capabilities compared to digital approaches. This paper provides a comprehensive review of the research works related to optical image hiding and watermarking techniques conducted in the past decade. The past research works have focused on two major aspects: various optical systems for image hiding, and the methods for embedding the optical system output into a host image. A summary of the state-of-the-art works is made from these two perspectives.

Connected through hyperedge HE-1 (induced by source paper "multiple image encryption and hiding with an optical diffractive neural network"):
  with members:
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "all optical machine learning using diffractive deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "does deep learning always outperform simple linear regression in optical imaging"):
  with members:
    - "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"
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "solving fourier ptychographic imaging problems via neural network modeling and tensorflow"
    - "lensless computational imaging through deep learning"
    - "deep learning microscopy"

Connected through hyperedge HE-3 (induced by source paper "a parallel optical image security system with cascaded phase only masks"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"

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)