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: "supervised learning in spiking neural networks for precise temporal encoding"
  Abstract: Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

Connected through hyperedge HE-1 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  with members:
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep convolutional spiking neural networks for image classification"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"

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"
    - "deep networks incorporating spiking neural dynamics"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "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 "federated neuromorphic learning of spiking neural networks for low power edge intelligence"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "robust and communication efficient federated learning from non iid data"
    - "supervised learning in spiking neural networks with phase change memory synapses"
    - "energy and policy considerations for deep learning in nlp"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "signsgd compressed optimisation for non convex problems"
    - "sparse communication for distributed gradient descent"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-4 (induced by source paper "an online supervised learning algorithm based on triple spikes for spiking neural networks"):
  with members:
    - "2013 special issue a neuromorphic vlsi design for spike timing and rate based synaptic plasticity"

Connected through hyperedge HE-5 (induced by source paper "training dynamic exponential family models with causal and lateral dependencies for generalized neuromorphic computing"):
  with members:
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "a brief introduction to machine learning for engineers"
    - "training deep spiking neural networks using backpropagation"
    - "network plasticity as bayesian inference"

Connected through hyperedge HE-6 (induced by source paper "pre synaptic pool modification pspm a supervised learning procedure for spiking neural networks"):
  with members:
    - "a spiking neural network with local learning rules derived from nonnegative similarity matching"
    - "sparse coding by spiking neural networks convergence theory and computational results"

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)