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: "supervised learning in multilayer spiking neural networks"
  Abstract: We introduce a supervised learning algorithm for multilayer spiking neural networks. The algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons firing multiple spikes in artificial neural networks with hidden layers. It can also, in principle, be used with any linearizable neuron model and allows different coding schemes of spike train patterns. The algorithm is applied successfully to classic linearly nonseparable benchmarks such as the XOR problem and the Iris data set, as well as to more complex classification and mapping problems. The algorithm has been successfully tested in the presence of noise, requires smaller networks than reservoir computing, and results in faster convergence than existing algorithms for similar tasks such as SpikeProp.

Connected through hyperedge HE-1 (induced by source paper "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"):
  with members:
    - "deep learning in spiking neural networks"
    - "spiking deep residual network"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "lstm a search space odyssey"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"

Connected through hyperedge HE-2 (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"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

Connected through hyperedge HE-3 (induced by source paper "fast and deep neuromorphic learning with time to first spike coding"):
  with members:
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "deep spiking neural network with spike count based learning rule"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "spinnaker 2 a 10 million core processor system for brain simulation and machine learning"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"

Connected through hyperedge HE-4 (induced by source paper "temporal coding in spiking neural networks with alpha synaptic function"):
  with members:
    - "deep learning in spiking neural networks"
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "evolutionary neural hybrid agents for architecture search"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "training multilayer spiking neural networks using normad based spatio temporal error backpropagation"):
  with members:
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-6 (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"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "metaheuristic design of feedforward neural networks"):
  with members:
    - "ensemble of heterogeneous flexible neural trees using multiobjective genetic programming"
    - "deep learning in neural networks"
    - "simultaneous optimization of neural network weights and active nodes using metaheuristics"
    - "a brief review of nature inspired algorithms for optimization"
    - "analysis of a nature inspired firefly algorithm based back propagation neural network training"
    - "meta learning evolutionary artificial neural networks"
    - "on classes of functions for which no free lunch results hold"
    - "reinforcement learning a survey"

Connected through hyperedge HE-8 (induced by source paper "an online structural plasticity rule for generating better reservoirs"):
  with members:
    - "learning spike time codes through morphological learning with binary synapses"
    - "an online unsupervised structural plasticity algorithm for spiking neural networks"
    - "learning spatiotemporally encoded pattern transformations in structured spiking neural networks"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"
    - "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites"

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)