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: "pre synaptic pool modification pspm a supervised learning procedure for spiking neural networks"
  Abstract: A central question in neuroscience is how to develop realistic models that predict output firing behavior based on provided external stimulus. Given a set of external inputs and a set of output spike trains, the objective is to discover a network structure which can accomplish the transformation as accurately as possible. Due to the difficulty of this problem in its most general form, approximations have been made in previous work. Past approximations have sacrificed network size, recurrence, allowed spiked count, or have imposed layered network structure. Here we present a learning rule without these sacrifices, which produces a weight matrix of a leaky integrate-and-fire (LIF) network to match the output activity of both deterministic LIF networks as well as probabilistic integrate-and-fire (PIF) networks. Inspired by synaptic scaling, our pre-synaptic pool modification (PSPM) algorithm outputs deterministic, fully recurrent spiking neural networks that can provide a novel generative model for given spike trains. Similarity in output spike trains is evaluated with a variety of metrics including a van-Rossum like measure and a numerical comparison of inter-spike interval distributions. Application of our algorithm to randomly generated networks improves similarity to the reference spike trains on both of these stated measures. In addition, we generated LIF networks that operate near criticality when trained on critical PIF outputs. Our results establish that learning rules based on synaptic homeostasis can be used to represent input-output relationships in fully recurrent spiking neural networks.

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)