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 spiking network that learns to extract spike signatures from speech signals"
  Abstract: Spiking neural networks (SNNs) with adaptive synapses reflect core properties of biological neural networks. Speech recognition, as an application involving audio coding and dynamic learning, provides a good test problem to study SNN functionality. We present a simple, novel, and efficient nonrecurrent SNN that learns to convert a speech signal into a spike train signature. The signature is distinguishable from signatures for other speech signals representing different words, thereby enabling digit recognition and discrimination in devices that use only spiking neurons. The method uses a small, nonrecurrent SNN consisting of Izhikevich neurons equipped with spike timing dependent plasticity (STDP) and biologically realistic synapses. This approach introduces an efficient and fast network without error-feedback training, although it does require supervised training. The new simulation results produce discriminative spike train patterns for spoken digits in which highly correlated spike trains belong to the same category and low correlated patterns belong to different categories. The proposed SNN is evaluated using a spoken digit recognition task where a subset of the Aurora speech dataset is used. The experimental results show that the network performs well in terms of accuracy rate and complexity.

Connected through hyperedge HE-1 (induced by source paper "deep learning in spiking neural networks"):
  with members:
    - "representation learning using event based stdp"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-2 (induced by source paper "deep spiking neural networks for large vocabulary automatic speech recognition"):
  with members:
    - "a tandem learning rule for efficient and rapid inference on deep spiking neural networks"
    - "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"
    - "deep learning in spiking neural networks"
    - "surrogate gradient learning in spiking neural networks"
    - "comprehensive snn compression using admm optimization and activity regularization"
    - "espnet end to end speech processing toolkit"
    - "acoustic and textual data augmentation for improved asr of code switching speech"
    - "going deeper in spiking neural networks vgg and residual architectures"

Connected through hyperedge HE-3 (induced by source paper "an efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks"):
  with members:
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "slayer spike layer error reassignment in time"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "you only look once unified real time object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "neural population coding for effective temporal classification"):
  with members:
    - "deep learning in spiking neural networks"

Connected through hyperedge HE-5 (induced by source paper "predicting performance using approximate state space model for liquid state machines"):
  with members:
    - "unsupervised heart rate estimation in wearables with liquid states and a probabilistic readout"

Connected through hyperedge HE-6 (induced by source paper "unsupervised heart rate estimation in wearables with liquid states and a probabilistic readout"):
  with members:
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-7 (induced by source paper "bp stdp approximating backpropagation using spike timing dependent plasticity"):
  with members:
    - "representation learning using event based stdp"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "deep spiking networks"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-8 (induced by source paper "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "acquisition of visual features through probabilistic spike timing dependent plasticity"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training a hidden markov model with a bayesian spiking neural network"
    - "training deep spiking neural networks using backpropagation"
    - "deep learning in neural networks"
    - "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)