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: "optoelectronic reservoir computing"
  Abstract: Reservoir computing is a recently introduced, highly efficient bio-inspired approach for processing time dependent data. The basic scheme of reservoir computing consists of a non linear recurrent dynamical system coupled to a single input layer and a single output layer. Within these constraints many implementations are possible. Here we report an optoelectronic implementation of reservoir computing based on a recently proposed architecture consisting of a single non linear node and a delay line. Our implementation is sufficiently fast for real time information processing. We illustrate its performance on tasks of practical importance such as nonlinear channel equalization and speech recognition, and obtain results comparable to state of the art digital implementations.

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"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "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"

Connected through hyperedge HE-2 (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"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "risk bounds for reservoir computing"):
  with members:
    - "differentiable reservoir computing"
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "theory and algorithms for forecasting time series"
    - "spectrally normalized margin bounds for neural networks"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "difference between memory and prediction in linear recurrent networks"
    - "the asymptotic performance of linear echo state neural networks"

Connected through hyperedge HE-4 (induced by source paper "large scale optical neural networks based on photoelectric multiplication"):
  with members:
    - "on chip optical convolutional neural networks"
    - "all optical machine learning using diffractive deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "neural message passing for quantum chemistry"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "recent trends in deep learning based natural language processing"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "differentiable reservoir computing"):
  with members:
    - "echo state networks are universal"
    - "reservoir computing universality with stochastic inputs"
    - "universal discrete time reservoir computers with stochastic inputs and linear readouts using non homogeneous state affine systems"
    - "the asymptotic performance of linear echo state neural networks"
    - "nonlinear memory capacity of parallel time delay reservoir computers in the processing of multidimensional signals"

Connected through hyperedge HE-6 (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"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "difference between memory and prediction in linear recurrent networks"
    - "embodiment of learning in electro optical signal processors"

Connected through hyperedge HE-7 (induced by source paper "design of optical neural networks with component imprecisions"):
  with members:
    - "deep complex networks"
    - "tunable efficient unitary neural networks eunn and their application to rnns"
    - "robustness of classifiers from adversarial to random noise"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "a substrate independent framework to characterise reservoir computers"):
  with members:
    - "reservoir computing with a single time delay autonomous boolean node"
    - "when does a physical system compute"
    - "nano scale reservoir computing"

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.ET(Emerging Technologies)