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 objective function for stdp"
  Abstract: We introduce a predictive objective function for the rate aspect of spike-timing dependent plasticity (STDP), i.e., ignoring the effects of synchrony of spikes but looking at spiking {\em rate changes}. The proposed weight update is proportional to the presynaptic spiking (or firing) rate times the {\em temporal change} of the integrated postsynaptic activity. We present an intuitive explanation for the relationship between spike-timing and weight change that arises when the weight change follows this rule. Spike-based simulations agree with the proposed relationship between spike timing and the temporal change of postsynaptic activity. They show a strong correlation between the biologically observed STDP behavior and the behavior obtained from simulations where the weight change follows the gradient of the predictive objective function.

Connected through hyperedge HE-1 (induced by source paper "spatio temporal backpropagation for training high performance spiking neural networks"):
  with members:
    - "a self driving robot using deep convolutional neural networks on neuromorphic hardware"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "spiking deep networks with lif neurons"
    - "gated feedback recurrent neural networks"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-2 (induced by source paper "boltzmann machines for time series"):
  with members:
    - "boltzmann machines and energy based models"
    - "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences"
    - "learning binary or real valued time series via spike timing dependent plasticity"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "learning dynamic boltzmann machines with spike timing dependent plasticity"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-3 (induced by source paper "temporally efficient deep learning with spikes"):
  with members:
    - "deep spiking networks"
    - "sigma delta quantized networks"
    - "deep counter networks for asynchronous event based processing"
    - "fast and efficient asynchronous neural computation with adapting spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-4 (induced by source paper "training deep spiking neural networks using backpropagation"):
  with members:
    - "deep spiking networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "spiking deep networks with lif neurons"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "equilibrium propagation bridging the gap between energy based models and backpropagation"):
  with members:
    - "unbiased online recurrent optimization"
    - "towards deep learning with spiking neurons in energy based models with contrastive hebbian plasticity"
    - "feedforward initialization for fast inference of deep generative networks is biologically plausible"
    - "early inference in energy based models approximates back propagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "why are deep nets reversible a simple theory with implications for training"
    - "training recurrent networks online without backtracking"
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-6 (induced by source paper "learning binary or real valued time series via spike timing dependent plasticity"):
  with members:
    - "regularized dynamic boltzmann machine with delay pruning for unsupervised learning of temporal sequences"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "learning dynamic boltzmann machines with spike timing dependent plasticity"

Connected through hyperedge HE-7 (induced by source paper "deep spiking networks"):
  with members:
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "spiking deep networks with lif neurons"

Connected through hyperedge HE-8 (induced by source paper "early inference in energy based models approximates back propagation"):
  with members:
    - "stdp as presynaptic activity times rate of change of postsynaptic activity"
    - "why are deep nets reversible a simple theory with implications for training"
    - "towards biologically plausible deep learning"

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)