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: "a stochastic approach to stdp"
  Abstract: We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay (exp-decay) generator array and a STDP adaptor array. The weight values are stored in a digital memory, and the STDP adaptor w ill send these values to the exp-decay generator using a digital spike of which the duration is modulated according to these values. The exp-decay generator will then generate an exponential decay, which will be used by the STDP adaptor for performing the weight adaption. The exponential decay, which is computational expensive, is efficiently implemented by using a novel stochastic approach. This stochastic approach was fully analysed and characterised. We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors and exp-decay generators with only one physical adaptor and exp-decay generator respectively. We have validated our stochastic STDP approach with measurement results of a balanced excitation experiment. In that experiment, the competition (induced by STDP) between the synapses can establish a bimodal distribution of the synaptic weights: either towards zero (weak) or the maximum (strong) values. Our stochastic approach is therefore ideal for implementing the STDP learning rule in large-scale spiking neural networks running in real time.

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)