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: "synaptic plasticity dynamics for deep continuous local learning decolle"
  Abstract: A growing body of work underlines striking similarities between biological neural networks and recurrent, binary neural networks. A relatively smaller body of work, however, discusses similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely caused by the discrepancy between the dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Learning algorithms that approximate gradient backpropagation using locally synthesized gradients can overcome this challenge. Here, we show that synthetic gradients enable the derivation of Deep Continuous Local Learning (DECOLLE) in spiking neural networks. DECOLLE is capable of learning deep spatio-temporal representations from spikes relying solely on local information. Synaptic plasticity rules are derived systematically from user-defined cost functions and neural dynamics by leveraging existing autodifferentiation methods of machine learning frameworks. We benchmark our approach on the MNIST and the event-based neuromorphic DvsGesture dataset, on which DECOLLE performs comparably to the state-of-the-art. DECOLLE networks provide continuously learning machines that are relevant to biology and supportive of event-based, low-power computer vision architectures matching the accuracies of conventional computers on tasks where temporal precision and speed are essential.

Connected through hyperedge HE-1 (induced by source paper "exploiting neuron and synapse filter dynamics in spatial temporal learning of deep spiking neural network"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "embodied event driven random backpropagation"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "direct training for spiking neural networks faster larger better"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-2 (induced by source paper "embodied synaptic plasticity with online reinforcement learning"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "deep rewiring training very sparse deep networks"
    - "proximal policy optimization algorithms"
    - "hamiltonian synaptic sampling in a model for reward gated network plasticity"
    - "asynchronous methods for deep reinforcement learning"
    - "camkii activation supports reward based neural network optimization through hamiltonian sampling"
    - "closed loop interactions between spiking neural network and robotic simulators based on music and ros"

Connected through hyperedge HE-3 (induced by source paper "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "measuring abstract reasoning in neural networks"
    - "lstm a search space odyssey"
    - "understanding synthetic gradients and decoupled neural interfaces"
    - "deep rewiring training very sparse deep networks"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learning to reinforcement learn"

Connected through hyperedge HE-4 (induced by source paper "embodied event driven random backpropagation"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "decoupled neural interfaces using synthetic gradients"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"

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)