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: "magnetic tunnel junction mimics stochastic cortical spiking neurons"
  Abstract: Brain-inspired computing architectures attempt to mimic the computations performed in the neurons and the synapses in the human brain in order to achieve its efficiency in learning and cognitive tasks. In this work, we demonstrate the mapping of the probabilistic spiking nature of pyramidal neurons in the cortex to the stochastic switching behavior of a Magnetic Tunnel Junction in presence of thermal noise. We present results to illustrate the efficiency of neuromorphic systems based on such probabilistic neurons for pattern recognition tasks in presence of lateral inhibition and homeostasis. Such stochastic MTJ neurons can also potentially provide a direct mapping to the probabilistic computing elements in Belief Networks for performing regenerative tasks.

Connected through hyperedge HE-1 (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"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"

Connected through hyperedge HE-2 (induced by source paper "energy efficient stochastic computing with superparamagnetic tunnel junctions"):
  with members:
    - "low barrier magnet design for efficient hardware binary stochastic neurons"
    - "p bits for probabilistic spin logic"
    - "voltage driven building block for hardware belief networks"
    - "implementing bayesian networks with embedded stochastic mram"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "reservoir computing using stochastic p bits"
    - "hardware driven nonlinear activation for stochastic computing based deep convolutional neural networks"

Connected through hyperedge HE-3 (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"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "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-4 (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"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-5 (induced by source paper "design of a low voltage analog to digital converter using voltage controlled stochastic switching of low barrier nanomagnets"):
  with members:
    - "mesl proposal for a non volatile cascadable magneto electric spin logic"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"

Connected through hyperedge HE-6 (induced by source paper "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"):
  with members:
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "magnetic skyrmion based synaptic devices"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "r dbn a resistive deep belief network architecture leveraging the intrinsic behavior of probabilistic devices"):
  with members:
    - "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"

Connected through hyperedge HE-8 (induced by source paper "voltage driven domain wall motion based neuro synaptic devices for dynamic on line learning"):
  with members:
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "spin orbit torque induced spike timing dependent plasticity"

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)