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: "fatiguing stdp learning from spike timing codes in the presence of rate codes"
  Abstract: Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates. In this article, we propose a spike-timing-dependent learning rule that allows a neuron to learn from the temporally-coded information despite the presence of rate codes. Our long-term plasticity rule makes use of short-term synaptic fatigue dynamics. We show analytically that, in contrast to conventional STDP rules, our fatiguing STDP (FSTDP) helps learn the temporal code, and we derive the necessary conditions to optimize the learning process. We showcase the effectiveness of FSTDP in learning spike-timing correlations among processes of different rates in synthetic data. Finally, we use FSTDP to detect correlations in real-world weather data from the United States in an experimental realization of the algorithm that uses a neuromorphic hardware platform comprising phase-change memristive devices. Taken together, our analyses and demonstrations suggest that FSTDP paves the way for the exploitation of the spike-based strengths of SNNs in real-world applications.

Connected through hyperedge HE-1 (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"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"
    - "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-2 (induced by source paper "deep networks incorporating spiking 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"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "using the output embedding to improve language models"
    - "training deep spiking neural networks using backpropagation"

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)