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"
  Abstract: A growing body of work underlines striking similarities between spiking neural networks modeling biological networks and recurrent, binary neural networks. A relatively smaller body of work, however, discuss similarities between learning dynamics employed in deep artificial neural networks and synaptic plasticity in spiking neural networks. The challenge preventing this is largely due to the discrepancy between dynamical properties of synaptic plasticity and the requirements for gradient backpropagation. Here, we demonstrate that deep learning algorithms that locally approximate the gradient backpropagation updates using locally synthesized gradients overcome this challenge. Locally synthesized gradients were initially proposed to decouple one or more layers from the rest of the network so as to improve parallelism. Here, we exploit these properties to derive gradient-based learning rules in spiking neural networks. Our approach results in highly efficient spiking neural networks and synaptic plasticity capable of training deep neural networks. Furthermore, our method utilizes existing autodifferentation methods in machine learning frameworks to systematically derive synaptic plasticity rules from task-relevant cost functions and neural dynamics. We benchmark our approach on the MNIST and DVS Gestures dataset, and report state-of-the-art results on the latter. Our results provide continuously learning machines that are not only relevant to biology, but suggestive of a brain-inspired computer architecture that matches the performances of GPUs on target tasks.

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 decolle"
    - "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 "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"
    - "deep neural network optimized to resistive memory with nonlinear current voltage characteristics"

Connected through hyperedge HE-3 (induced by source paper "surrogate gradient learning in spiking neural networks"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "deep networks incorporating spiking neural dynamics"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "temporally efficient deep learning with spikes"
    - "deep supervised learning using local errors"

Connected through hyperedge HE-4 (induced by source paper "technical report supervised training of convolutional spiking neural networks with pytorch"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"

Connected through hyperedge HE-5 (induced by source paper "embodied synaptic plasticity with online reinforcement learning"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "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-6 (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 decolle"
    - "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-7 (induced by source paper "on chip few shot learning with surrogate gradient descent on a neuromorphic processor"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "error triggered three factor learning dynamics for crossbar arrays"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "deep supervised learning using local errors"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine the symmetries of the deep learning channel"

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)