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: "gibbs sampling with low power spiking digital neurons"
  Abstract: Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inference and learning in these algorithms uses a Markov Chain Monte Carlo procedure called Gibbs sampling. A sigmoidal function forms the kernel of this sampler which can be realized from the firing statistics of noisy integrate-and-fire neurons on a neuromorphic VLSI substrate. This paper demonstrates such an implementation on an array of digital spiking neurons with stochastic leak and threshold properties for inference tasks and presents some key performance metrics for such a hardware-based sampler in both the generative and discriminative contexts.

Connected through hyperedge HE-1 (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-2 (induced by source paper "event driven random back propagation enabling neuromorphic deep learning machines"):
  with members:
    - "learning in the machine random backpropagation and the deep learning channel"
    - "forward table based presynaptic event triggered spike timing dependent plasticity"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "how important is weight symmetry in backpropagation"
    - "difference target propagation"

Connected through hyperedge HE-3 (induced by source paper "approxdbn approximate computing for discriminative deep belief networks"):
  with members:
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-4 (induced by source paper "convolutional networks for fast energy efficient neuromorphic computing"):
  with members:
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "adjustable bounded rectifiers towards deep binary representations"
    - "learning both weights and connections for efficient neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "striving for simplicity the all convolutional net"
    - "matconvnet convolutional neural networks for matlab"
    - "flattened convolutional neural networks for feedforward acceleration"

Connected through hyperedge HE-5 (induced by source paper "stochastic synapses enable efficient brain inspired learning machines"):
  with members:
    - "real time unsupervised learning of visual stimuli in neuromorphic vlsi systems"
    - "network plasticity as bayesian inference"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (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"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-7 (induced by source paper "a nonparametric framework for quantifying generative inference on neuromorphic systems"):
  with members:
    - "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.NE(Neural and Evolutionary Computing)