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 "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.

Includes members:
  Member "on chip optical convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy")
  Member "all optical machine learning using diffractive deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation")
  Member "pcnna a photonic convolutional neural network accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy")
  Member "reinforcement learning in a large scale photonic recurrent neural network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction")
  Member "toward fast neural computing using all photonic phase change spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy")
  Member "principles of neuromorphic photonics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks")
  Member "be careful what you backpropagate a case for linear output activations gradient boosting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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.NE(Neural and Evolutionary Computing)