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 based electronic neuron"
  Abstract: A device based on current-induced spin-orbit torque (SOT) that functions as an electronic neuron is proposed in this work. The SOT device implements an artificial neuron's thresholding (transfer) function. In the first step of a two-step switching scheme, a charge current places the magnetization of a nano-magnet along the hard-axis, i.e., an unstable point for the magnet. In the second step, the SOT device (neuron) receives a current (from the synapses) which moves the magnetization from the unstable point to one of the two stable states. The polarity of the synaptic current encodes the excitatory and inhibitory nature of the neuron input and determines the final orientation of the magnetization. A resistive crossbar array, functioning as synapses, generates a bipolar current that is a weighted sum of the inputs. The simulation of a two layer feed-forward artificial neural network based on the SOT electronic neuron shows that it consumes ∼3× lower power than a 45 nm digital CMOS implementation, while reaching ∼80% accuracy in the classification of 100 images of handwritten digits from the MNIST dataset.

Connected through hyperedge HE-1 (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"
    - "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"
    - "limits on fundamental limits to computation"

Connected through hyperedge HE-2 (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-3 (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"
    - "spin orbit torque induced spike timing dependent plasticity"

Connected through hyperedge HE-4 (induced by source paper "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"):
  with members:
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"):
  with members:
    - "deep learning in neural networks"
    - "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-6 (induced by source paper "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"):
  with members:
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-7 (induced by source paper "magnetic tunnel junction mimics stochastic cortical spiking neurons"):
  with members:
    - "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)