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: "an online supervised learning algorithm based on triple spikes for spiking neural networks"
  Abstract: Using precise times of every spike, spiking supervised learning has more effects on complex spatial-temporal pattern than that only through neuronal firing rates. The purpose of spiking supervised learning after temporal encoding is to make neural networks emit desired spike trains with precise firing time. Existing algorithms of spiking supervised learning have excellent performances, but mechanisms of them are most in an offline pattern or still have some problems. Based on an online regulative mechanism of biological neuronal synapses, this paper proposes an online supervised learning algorithm of multiple spike trains for spiking neural networks. The proposed algorithm can make a regulation of weights as soon as firing time of an output spike is obtained. Relationship among desired output, actual output and input spike trains is firstly analyzed and synthesized to select spikes simply and correctly for a direct regulation, and then a computational method is constructed based on simple triple spikes using this direct regulation. Results of experiments show that this online supervised algorithm improves learning performance obviously compared with offline pattern and has higher learning accuracy and efficiency than other learning algorithms.

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)