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: "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
  Abstract: Deep spiking neural networks are becoming increasingly powerful tools for cognitive computing platforms. However, most of the existing studies on such computing models are developed with limited insights on the underlying hardware implementation, resulting in area and power expensive designs. Although several neuromimetic devices emulating neural operations have been proposed recently, their functionality has been limited to very simple neural models that may prove to be inefficient at complex recognition tasks. In this paper, we venture into the relatively unexplored area of utilizing the inherent device stochasticity of such neuromimetic devices to model complex neural functionalities in a probabilistic framework in the time domain. We consider the implementation of a deep spiking neural network capable of performing high-accuracy and low-latency classification tasks, where the neural computing unit is enabled by the stochastic switching behavior of a magnetic tunnel junction. The simulation studies indicate an energy improvement of    $20 \times $    over a baseline CMOS design in 45-nm technology.

Connected through hyperedge HE-1 (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-2 (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"
    - "stochastic spiking neural networks enabled by magnetic tunnel junctions from nontelegraphic to telegraphic switching regimes"
    - "hardware emulation of stochastic p bits for invertible logic"
    - "vlsi implementation of deep neural network using integral stochastic computing"

Connected through hyperedge HE-3 (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"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-4 (induced by source paper "synthesizing images from spatio temporal representations using spike based backpropagation"):
  with members:
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "representation learning using event based stdp"
    - "direct training for spiking neural networks faster larger better"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-5 (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"
    - "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-6 (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"
    - "proposal for an all spin artificial neural network emulating neural and synaptic functionalities through domain wall motion in ferromagnets"
    - "spin orbit torque induced spike timing dependent plasticity"

Connected through hyperedge HE-7 (induced by source paper "a photonic in memory computing primitive for spiking neural networks using phase change materials"):
  with members:
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "toward fast neural computing using all photonic phase change spiking neurons"
    - "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-8 (induced by source paper "in situ stochastic training of mtj crossbar based neural networks"):
  with members:
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

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)