human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deep networks incorporating spiking neural dynamics"):
  Abstract: Neural networks have become the key technology of Artificial Intelligence (AI) that contributed to breakthroughs in several machine learning tasks, primarily owing to advances in Artificial Neural Networks (ANNs). Neural networks incorporating spiking neurons have held great promise because of their brain-inspired temporal dynamics and high power efficiency, however scaling out and training deep Spiking Neural Networks (SNNs) remained significant challenges - they still fall behind ANNs in terms of accuracy on many traditional learning tasks. In this paper, we propose an alternative perspective on the spiking neuron as a particular ANN construct called Spiking Neural Unit (SNU). Specifically, the SNU casts the stateful temporal spiking neuron, of a LIF type, as a recurrent ANN unit, analogous to LSTMs and GRUs. Moreover, by introducing the concept of proportional reset, we generalize the LIF dynamics to a variant called soft SNU (sSNU). The proposed family of units has a series of benefits. Firstly, the ANN training methods, such as backpropagation through time, naturally apply to them and enable a simple approach for successful training of deep spiking networks. For example, a 4- or 7-layer SNN trained on a temporal version of MNIST dataset provides higher accuracy in comparison to RNNs, LSTM- and GRU-based networks of similar architecture. Secondly, for the task of polyphonic music prediction on the JSB dataset, an sSNU-based network surpasses the state-of-the-art performance of RNNs, LSTM- and GRU-based networks. The novel family of units introduced in this paper bridges the biologically-inspired SNNs with ANNs. It provides a systematic methodology for implementing and training deep networks incorporating spiking dynamics that achieve accuracies as high, or better than, state-of-the-art ANNs. Thus, it opens a new avenue for the widespread adoption of SNNs to practical applications.

Includes members:
  Member "long short term memory and learning to learn in networks of spiking neurons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "spatio temporal backpropagation for training high performance spiking neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploring adversarial attack in spiking neural networks with spike compatible gradient")
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "fatiguing stdp learning from spike timing codes in the presence of rate codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning incorporating biologically inspired neural dynamics")
  Member "bp stdp approximating backpropagation using spike timing dependent plasticity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule")
  Member "bio inspired unsupervised learning of visual features leads to robust invariant object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification")
  Member "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rmp snns residual membrane potential neuron for enabling deeper high accuracy and low latency spiking neural networks")
  Member "using the output embedding to improve language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta embeddings based on self attention")

Question: Please tell me which class the source paper 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)