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: "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"
  Abstract: A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (i.e. presynaptic terminals), 8192 synapses and 64 output channels (i.e. neurons). Biologically realistic neuron and synapse dynam- ics are achieved via a faithful translation of the behavioural equations to SC circuits. As leakage currents significantly affect circuit behaviour at this technology node, dedicated compensation techniques are employed to achieve biological-realtime operation, with faithful reproduction of time constants of several 100 ms at room temperature. Power draw of the overall system is 1.9 mW.

Connected through hyperedge HE-1 (induced by source paper "design space exploration of hardware spiking neurons for embedded artificial intelligence"):
  with members:
    - "deep learning in spiking neural networks"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "hfirst a temporal approach to object recognition"

Connected through hyperedge HE-2 (induced by source paper "morphic a 65 nm 738k synapse mm 2 quad core binary weight digital neuromorphic processor with stochastic spike driven online learning"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "minimum energy quantized neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "supervised learning based on temporal coding in spiking neural networks"

Connected through hyperedge HE-3 (induced by source paper "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"):
  with members:
    - "minimum energy quantized neural networks"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "memory and information processing in neuromorphic systems"
    - "switched capacitor realization of presynaptic short term plasticity and stop learning synapses in 28 nm cmos"
    - "biologically inspired spiking neurons piecewise linear models and digital implementation"

Connected through hyperedge HE-4 (induced by source paper "efficient reward based structural plasticity on a spinnaker 2 prototype"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "large scale neuromorphic spiking array processors a quest to mimic the brain"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "neuromorphic hardware in the loop training a deep spiking network on the brainscales wafer scale system"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-5 (induced by source paper "scaling mixed signal neuromorphic processors to 28 nm fd soi technologies"):
  with members:
    - "memory and information processing in neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-6 (induced by source paper "mean field approach for configuring population dynamics on a biohybrid neuromorphic system"):
  with members:
    - "switched capacitor realization of presynaptic short term plasticity and stop learning synapses in 28 nm cmos"

Connected through hyperedge HE-7 (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-8 (induced by source paper "mapping generative models onto a network of digital spiking neurons"):
  with members:
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "gibbs sampling with low power spiking digital neurons"
    - "event driven contrastive divergence for spiking neuromorphic systems"

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)