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: "bp stdp approximating backpropagation using spike timing dependent plasticity"
  Abstract: The problem of training spiking neural networks (SNNs) is a necessary precondition to understanding computations within the brain, a field still in its infancy. Previous work has shown that supervised learning in multi-layer SNNs enables bio-inspired networks to recognize patterns of stimuli through hierarchical feature acquisition. Although gradient descent has shown impressive performance in multi-layer (and deep) SNNs, it is generally not considered biologically plausible and is also computationally expensive. This paper proposes a novel supervised learning approach based on an event-based spike-timing-dependent plasticity (STDP) rule embedded in a network of integrate-and-fire (IF) neurons. The proposed temporally local learning rule follows the backpropagation weight change updates applied at each time step. This approach enjoys benefits of both accurate gradient descent and temporally local, efficient STDP. Thus, this method is able to address some open questions regarding accurate and efficient computations that occur in the brain. The experimental results on the XOR problem, the Iris data, and the MNIST dataset demonstrate that the proposed SNN performs as successfully as the traditional NNs. Our approach also compares favorably with the state-of-the-art multi-layer SNNs.

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"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-2 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "embodied event driven random backpropagation"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "direct training for spiking neural networks faster larger better"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"

Connected through hyperedge HE-3 (induced by source paper "minimal spiking neuron for solving multi label classification tasks"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "demonstrating advantages of neuromorphic computation a pilot study"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-4 (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-5 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "fast and deep neuromorphic learning with time to first spike coding"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "deep learning incorporating biologically inspired neural dynamics"):
  with members:
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "lstm a search space odyssey"
    - "fatiguing stdp learning from spike timing codes in the presence of rate codes"
    - "mask r cnn"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "using the output embedding to improve language models"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-8 (induced by source paper "a novel method for extracting interpretable knowledge from a spiking neural classifier with time varying synaptic weights"):
  with members:
    - "deep learning in spiking neural networks"
    - "interpreting cnns via decision trees"
    - "a unified approach to interpreting model predictions"
    - "distilling a neural network into a soft decision tree"
    - "why should i trust you explaining the predictions of any classifier"
    - "training deep spiking neural networks using backpropagation"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "striving for simplicity the all convolutional net"

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)