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: "spin orbit torque induced spike timing dependent plasticity"
  Abstract: Nanoelectronic devices that mimic the functionality of synapses are a crucial requirement for performing cortical simulations of the brain. In this work, we propose a ferromagnet-heavy metal heterostructure that employs spin-orbit torque to implement spike-timing dependent plasticity. The proposed device offers the advantage of decoupled spike transmission and programming current paths, thereby leading to reliable operation during online learning. Possible arrangement of such devices in a crosspoint architecture can pave the way for ultra-dense neural networks. Simulation studies indicate that the device has the potential of achieving pico-Joule level energy consumption (maximum 2 pJ per synaptic event) which is comparable to the energy consumption for synaptic events in biological synapses.

Connected through hyperedge HE-1 (induced by source paper "all spin bayesian neural networks"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "implementing bayesian networks with embedded stochastic mram"
    - "vibnn hardware acceleration of bayesian neural networks"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "resparc a reconfigurable and energy efficient architecture with memristive crossbars for deep spiking neural networks"
    - "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-2 (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"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"
    - "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-3 (induced by source paper "stochastic magnetoelectric neuron for temporal information encoding"):
  with members:
    - "p bits for probabilistic spin logic"
    - "design of a low voltage analog to digital converter using voltage controlled stochastic switching of low barrier nanomagnets"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "energy efficient memories using magneto electric switching of ferromagnets"
    - "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-4 (induced by source paper "dendritic inspired processing enables bio plausible stdp in compound binary synapses"):
  with members:
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "training deep spiking neural networks using backpropagation"
    - "an online structural plasticity rule for generating better reservoirs"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "a cmos spiking neuron for dense memristor synapse connectivity for brain inspired computing"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"

Connected through hyperedge HE-5 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"

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"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"
    - "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 "magnetic skyrmion based synaptic devices"):
  with members:
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "device and system level design considerations for analog non volatile memory based neuromorphic architectures"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

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"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

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)