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: "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.

Connected through hyperedge HE-1 (induced by source paper "surrogate gradient learning in spiking neural networks"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "temporally efficient deep learning with spikes"
    - "deep supervised learning using local errors"

Connected through hyperedge HE-2 (induced by source paper "the heidelberg spiking datasets for the systematic evaluation of spiking neural networks"):
  with members:
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "supervised learning in spiking neural networks for precise temporal encoding"
    - "blackbox a procedure for parallel optimization of expensive black box functions"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "low power neuromorphic hardware for signal processing applications"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "neuromorphic computing with multi memristive synapses"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "learning to recognize actions from limited training examples using a recurrent spiking neural model"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "binaryconnect training deep neural networks with binary weights during propagations"

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)