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: "all optical machine learning using diffractive deep neural networks"
  Abstract: Deep learning has been transforming our ability to execute advanced inference tasks using computers. Here we introduce a physical mechanism to perform machine learning by demonstrating an all-optical diffractive deep neural network (D2NN) architecture that can implement various functions following the deep learning–based design of passive diffractive layers that work collectively. We created 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products, as well as the function of an imaging lens at a terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can execute; will find applications in all-optical image analysis, feature detection, and object classification; and will also enable new camera designs and optical components that perform distinctive tasks using D2NNs.

Connected through hyperedge HE-1 (induced by source paper "noisy machines understanding noisy neural networks and enhancing robustness to analog hardware errors using distillation"):
  with members:
    - "zero shot knowledge transfer via adversarial belief matching"
    - "adversarially robust distillation"
    - "accurate deep neural network inference using computational phase change memory"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "neuromorphic computing with multi memristive synapses"
    - "mixed precision in memory computing"
    - "model compression via distillation and quantization"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"

Connected through hyperedge HE-2 (induced by source paper "analysis of diffractive optical neural networks and their integration with electronic neural networks"):
  with members:
    - "on chip optical convolutional 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"
    - "principles of neuromorphic photonics"
    - "be careful what you backpropagate a case for linear output activations gradient boosting"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-3 (induced by source paper "physics for neuromorphic computing"):
  with members:
    - "in situ learning harnessing intrinsic resistive memory variability through markov chain monte carlo sampling"
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "neuromorphic computing with multi memristive synapses"
    - "biological plausibility and stochasticity in scalable vo2 active memristor neurons"
    - "computing with networks of nonlinear mechanical oscillators"
    - "brain inspired photonic signal processor for generating periodic patterns and emulating chaotic systems"

Connected through hyperedge HE-4 (induced by source paper "express wavenet a low parameter optical neural network with random shift wavelet pattern"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "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"
    - "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-5 (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"
    - "review on optical image hiding and watermarking techniques"

Connected through hyperedge HE-6 (induced by source paper "design of task specific optical systems using broadband diffractive neural networks"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "class specific differential detection in diffractive optical neural networks improves inference accuracy"
    - "virtual histological staining of unlabelled tissue autofluorescence images via deep learning"
    - "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-7 (induced by source paper "class specific differential detection in diffractive optical neural networks improves inference accuracy"):
  with members:
    - "analysis of diffractive optical neural networks and their integration with electronic neural networks"
    - "on chip optical convolutional 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-8 (induced by source paper "efficient training and design of photonic neural network through neuroevolution"):
  with members:
    - "deep learning in spiking neural networks"
    - "design of optical neural networks with component imprecisions"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "end to end learning for self driving cars"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "deep learning in 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.NE(Neural and Evolutionary Computing)