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: "analysis of diffractive optical neural networks and their integration with electronic neural networks"
  Abstract: Optical machine learning offers advantages in terms of power efficiency, scalability, and computation speed. Recently, an optical machine learning method based on diffractive deep neural networks (D2NNs) has been introduced to execute a function as the input light diffracts through passive layers, designed by deep learning using a computer. Here, we introduce improvements to D2NNs by changing the training loss function and reducing the impact of vanishing gradients in the error back-propagation step. Using five phase-only diffractive layers, we numerically achieved a classification accuracy of 97.18% and 89.13% for optical recognition of handwritten digits and fashion products, respectively; using both phase and amplitude modulation (complex-valued) at each layer, our inference performance improved to 97.81% and 89.32%, respectively. Furthermore, we report the integration of D2NNs with electronic neural networks to create hybrid classifiers that significantly reduce the number of input pixels into an electronic network using an ultra-compact front-end D2NN with a layer-to-layer distance of a few wavelengths, also reducing the complexity of the successive electronic network. Using a five-layer phase-only D2NN jointly optimized with a single fully connected electronic layer, we achieved a classification accuracy of 98.71% and 90.04% for the recognition of handwritten digits and fashion products, respectively. Moreover, the input to the electronic network was compressed by >7.8 times down to 10 × 10 pixels. Beyond creating low-power and high-frame rate machine learning platforms, D2NN-based hybrid neural networks will find applications in smart optical imager and sensor design.

Connected through hyperedge HE-1 (induced by source paper "express wavenet a low parameter optical neural network with random shift wavelet pattern"):
  with members:
    - "an optical diffractive deep neural network with multiple frequency channels"
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "a parallel optical image security system with cascaded phase only masks"
    - "optical machine learning with incoherent light and a single pixel detector"
    - "all optical machine learning using diffractive deep neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "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"
    - "phase recovery and holographic image reconstruction using deep learning in 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 "class specific differential detection in diffractive optical neural networks improves inference accuracy"):
  with members:
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "pcnna a photonic convolutional neural network accelerator"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "a survey on deep learning in medical image analysis"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "an optical diffractive deep neural network with multiple frequency channels"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "comment on all optical machine learning using diffractive deep neural networks"
    - "response to comment on all optical machine learning using diffractive deep neural networks"
    - "emnist an extension of mnist to handwritten letters"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "an optical frontend for a convolutional neural network"):
  with members:
    - "photo realistic single image super resolution using a generative adversarial network"
    - "asp vision optically computing the first layer of convolutional neural networks using angle sensitive pixels"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "response to comment on all optical machine learning using diffractive deep neural networks"):
  with members:
    - "all optical machine learning using diffractive deep neural networks"
    - "comment on all optical machine learning using diffractive deep neural networks"
    - "adversarial examples in the physical world"
    - "towards deep neural network architectures robust to adversarial examples"

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.NE(Neural and Evolutionary Computing)