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: "encoding neural and synaptic functionalities in electron spin a pathway to efficient neuromorphic computing"
  Abstract: Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform everyday. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

Connected through hyperedge HE-1 (induced by source paper "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "surrogate gradient learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "vime variational information maximizing exploration"

Connected through hyperedge HE-3 (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"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"

Connected through hyperedge HE-4 (induced by source paper "convolutional neural networks utilizing multifunctional spin hall mtj neurons"):
  with members:
    - "a mixed signal architecture for convolutional neural networks"
    - "a proposal for energy efficient cellular neural network based on spintronic devices"

Connected through hyperedge HE-5 (induced by source paper "nonvolatile spintronic memory cells for neural networks"):
  with members:
    - "a mixed signal architecture for convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "toward fast neural computing using all photonic phase change spiking neurons"):
  with members:
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"

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)