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: "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
  Abstract: Over the past decade Spiking Neural Networks (SNN) have emerged as one of the popular architectures to emulate the brain. In SNN, information is temporally encoded and communication between neurons is accomplished by means of spikes. In such networks, spike-timing dependent plasticity mechanisms require the online programming of synapses based on the temporal information of spikes transmitted by spiking neurons. In this work, we propose a spintronic synapse with decoupled spike transmission and programming current paths. The spintronic synapse consists of a ferromagnet-heavy metal heterostructure where programming current through the heavy metal generates spin-orbit torque to modulate the device conductance. Low programming energy and fast programming times demonstrate the efficacy of the proposed device as a nanoelectronic synapse. We perform a simulation study based on an experimentally benchmarked device-simulation framework to demonstrate the interfacing of such spintronic synapses with CMOS neurons and learning circuits operating in transistor sub-threshold region to form a network of spiking neurons that can be utilized for pattern recognition problems.

Connected through hyperedge HE-1 (induced by source paper "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "adam a method for stochastic optimization"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-2 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"

Connected through hyperedge HE-3 (induced by source paper "benchmarking physical performance of neural inference circuits"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "benchmarking inverse rashba edelstein magnetoelectric devices for neuromorphic computing"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey of neuromorphic computing and neural networks in hardware"

Connected through hyperedge HE-4 (induced by source paper "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"):
  with members:
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "supervised learning based on temporal coding in spiking neural networks"

Connected through hyperedge HE-5 (induced by source paper "spin orbit torque based devices circuits and architectures"):
  with members:
    - "spin orbit torque devices for hardware security from deterministic to probabilistic regime"
    - "a model of an oscillatory neural network with multilevel neurons for pattern recognition and computing"
    - "vertex coloring of graphs via phase dynamics of coupled oscillatory networks"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"
    - "spin orbit torque induced spike timing dependent plasticity"
    - "stt snn a spin transfer torque based soft limiting non linear neuron for low power artificial neural networks"
    - "spin orbit torque based electronic neuron"
    - "limits on fundamental limits to computation"

Connected through hyperedge HE-6 (induced by source paper "composable probabilistic inference networks using mram based stochastic neurons"):
  with members:
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

Connected through hyperedge HE-7 (induced by source paper "snra a spintronic neuromorphic reconfigurable array for in circuit training and evaluation of deep belief networks"):
  with members:
    - "exploiting spin orbit torque devices as reconfigurable logic for circuit obfuscation"
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "vlsi implementation of deep neural network using integral stochastic computing"

Connected through hyperedge HE-8 (induced by source paper "polaritonic neuromorphic computing outperforms linear classifiers"):
  with members:
    - "physical reservoir computing built by spintronic devices for temporal information processing"
    - "all optical machine learning using diffractive deep neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

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)