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: "reinforcement learning in a large scale photonic recurrent neural network"
  Abstract: Photonic neural network implementation has been gaining considerable attention as a potentially disruptive future technology. Demonstrating learning in large-scale neural networks is essential to establish photonic machine learning substrates as viable information processing systems. Realizing photonic neural networks with numerous nonlinear nodes in a fully parallel and efficient learning hardware has been lacking so far. We demonstrate a network of up to 2025 diffractively coupled photonic nodes, forming a large-scale recurrent neural network. Using a digital micro mirror device, we realize reinforcement learning. Our scheme is fully parallel, and the passive weights maximize energy efficiency and bandwidth. The computational output efficiently converges, and we achieve very good performance.

Connected through hyperedge HE-1 (induced by source paper "optical reservoir computing using multiple light scattering for chaotic systems prediction"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "a substrate independent framework to characterise reservoir computers"
    - "echo state networks are universal"
    - "scaling up echo state networks with multiple light scattering"
    - "newma a new method for scalable model free online change point detection"
    - "using a reservoir computer to learn chaotic attractors with applications to chaos synchronization and cryptography"
    - "hardware for machine learning challenges and opportunities"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

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"
    - "all optical machine learning using diffractive deep neural networks"
    - "pcnna a photonic convolutional neural network accelerator"
    - "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 "recent advances in physical reservoir computing a review"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "echo state networks are universal"
    - "reservoir computing hardware with cellular automata"
    - "scaling up echo state networks with multiple light scattering"
    - "reservoir computing universality with stochastic inputs"
    - "the case for rejecting the memristor as a fundamental circuit element"
    - "reservoir computing using non uniform binary cellular automata"
    - "computing with networks of nonlinear mechanical oscillators"

Connected through hyperedge HE-4 (induced by source paper "implementation of optical deep neural networks using the fabry perot interferometer"):
  with members:
    - "large scale optical neural networks based on photoelectric multiplication"
    - "kronecker recurrent units"
    - "gated orthogonal recurrent units on learning to forget"
    - "the numerics of gans"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "searching for activation functions"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "all optical machine learning using diffractive deep neural networks"
    - "pcnna a photonic convolutional neural network accelerator"
    - "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-6 (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"
    - "all optical machine learning using diffractive deep neural networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "coupled nonlinear delay systems as deep convolutional neural networks"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "all optical machine learning using diffractive deep neural networks"
    - "difference between memory and prediction in linear recurrent networks"
    - "embodiment of learning in electro optical signal processors"
    - "optoelectronic reservoir computing"

Connected through hyperedge HE-8 (induced by source paper "fundamental aspects of noise in analog hardware neural networks"):
  with members:
    - "recent advances in physical reservoir computing a review"
    - "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.NE(Neural and Evolutionary Computing)