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: "stdp as presynaptic activity times rate of change of postsynaptic activity"
  Abstract: We introduce a weight update formula that is expressed only in terms of firing rates and their derivatives and that results in changes consistent with those associated with spike-timing dependent plasticity (STDP) rules and biological observations, even though the explicit timing of spikes is not needed. The new rule changes a synaptic weight in proportion to the product of the presynaptic firing rate and the temporal rate of change of activity on the postsynaptic side. These quantities are interesting for studying theoretical explanation for synaptic changes from a machine learning perspective. In particular, if neural dynamics moved neural activity towards reducing some objective function, then this STDP rule would correspond to stochastic gradient descent on that objective function.

Connected through hyperedge HE-1 (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"
    - "an objective function for stdp"
    - "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-2 (induced by source paper "using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks"):
  with members:
    - "towards biologically plausible deep learning"

Connected through hyperedge HE-3 (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"
    - "why are deep nets reversible a simple theory with implications for training"
    - "training recurrent networks online without backtracking"
    - "towards biologically plausible deep learning"
    - "an objective function for stdp"

Connected through hyperedge HE-4 (induced by source paper "towards a biologically plausible backprop"):
  with members:
    - "early inference in energy based models approximates back propagation"
    - "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-5 (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"
    - "an objective function for stdp"
    - "learning dynamic boltzmann machines with spike timing dependent plasticity"

Connected through hyperedge HE-6 (induced by source paper "early inference in energy based models approximates back propagation"):
  with members:
    - "why are deep nets reversible a simple theory with implications for training"
    - "towards biologically plausible deep learning"
    - "an objective function for stdp"

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)