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: "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
  Abstract: Stochastic spiking neural networks based on nanoelectronic spin devices can be a possible pathway to achieving "brainlike" compact and energy-effcient cognitive intelligence. The computational model attempt to exploit the intrinsic device stochasticity of nanoelectronic synaptic or neural components to perform learning or inference. However, there has been limited analysis on the scaling effect of stochastic spin devices and its impact on the operation of such stochastic networks at the system level. This work attempts to explore the design space and analyze the performance of nanomagnet-based stochastic neuromorphic computing architectures for magnets with different barrier heights. We illustrate how the underlying network architecture must be modified to account for the random telegraphic switching behavior displayed by magnets with low barrier heights as they are scaled into the superparamagnetic regime. We perform a device-to-system-level analysis on a deep neural-network architecture for a digit-recognition problem on the MNIST data set.

Connected through hyperedge HE-1 (induced by source paper "p bits for probabilistic spin logic"):
  with members:
    - "weighted p bits for fpga implementation of probabilistic circuits"
    - "low energy deep belief networks using intrinsic sigmoidal spintronic based probabilistic neurons"
    - "voltage driven building block for hardware belief networks"
    - "perspective memcomputing leveraging memory and physics to compute efficiently"
    - "stochastic spin orbit torque devices as elements for bayesian inference"
    - "hardware emulation of stochastic p bits for invertible logic"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"

Connected through hyperedge HE-2 (induced by source paper "autonomous probabilistic coprocessing with petaflips per second"):
  with members:
    - "composable probabilistic inference networks using mram based stochastic neurons"
    - "p bits for probabilistic spin logic"
    - "scrambled linear pseudorandom number generators"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "oscillator based ising machine"
    - "fpga hardware acceleration of monte carlo simulations for the ising model"
    - "janus ii a new generation application driven computer for spin system simulations"

Connected through hyperedge HE-3 (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"
    - "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"
    - "magnetic tunnel junction mimics stochastic cortical spiking neurons"

Connected through hyperedge HE-4 (induced by source paper "low barrier magnet design for efficient hardware binary stochastic neurons"):
  with members:
    - "composable probabilistic inference networks using mram based stochastic neurons"
    - "voltage driven building block for hardware belief networks"
    - "hardware emulation of stochastic p bits for invertible logic"
    - "vlsi implementation of deep neural network using integral stochastic computing"

Connected through hyperedge HE-5 (induced by source paper "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"):
  with members:
    - "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"
    - "stt snn a spin transfer torque based soft limiting non linear neuron for low power artificial neural networks"

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)