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: "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
  Abstract: Synaptic memory is considered to be the main element responsible for learning and cognition in humans. Although traditionally non-volatile long-term plasticity changes have been implemented in nanoelectronic synapses for neuromorphic applications, recent studies in neuroscience have revealed that biological synapses undergo meta-stable volatile strengthening followed by a long-term strengthening provided that the frequency of the input stimulus is sufficiently high. Such "memory strengthening" and "memory decay" functionalities can potentially lead to adaptive neuromorphic architectures. In this paper, we demonstrate the close resemblance of the magnetization dynamics of a Magnetic Tunnel Junction (MTJ) to short-term plasticity and long-term potentiation observed in biological synapses. We illustrate that, in addition to the magnitude and duration of the input stimulus, frequency of the stimulus plays a critical role in determining long-term potentiation of the MTJ. Such MTJ synaptic memory arrays can be utilized to create compact, ultra-fast and low power intelligent neural systems.

Connected through hyperedge HE-1 (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"
    - "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"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "magnetic skyrmion based synaptic devices"):
  with members:
    - "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"
    - "spin orbit torque induced spike timing dependent plasticity"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-4 (induced by source paper "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"):
  with members:
    - "spiking deep networks with lif 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"

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)