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: "autoencoder based holographic image restoration"
  Abstract: We propose a holographic image restoration method using an autoencoder, which is an artificial neural network. Because holographic reconstructed images are often contaminated by direct light, conjugate light, and speckle noise, the discrimination of reconstructed images may be difficult. In this paper, we demonstrate the restoration of reconstructed images from holograms that record page data in holographic memory and quick response codes by using the proposed method.

Connected through hyperedge HE-1 (induced by source paper "a practical tutorial on autoencoders for nonlinear feature fusion taxonomy models software and guidelines"):
  with members:
    - "self normalizing neural networks"
    - "adversarial symmetric variational autoencoder"
    - "time series compression based on adaptive piecewise recurrent autoencoder"
    - "relational autoencoder for feature extraction"
    - "medical image denoising using convolutional denoising autoencoders"
    - "variational autoencoders for semi supervised text classification"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "language as a latent variable discrete generative models for sentence compression"

Connected through hyperedge HE-2 (induced by source paper "convolutional neural network based regression for depth prediction in digital holography"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "convolutional neural network based data page classification for holographic memory"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural network based data page classification for holographic memory"):
  with members:
    - "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)