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: "event driven random back propagation enabling neuromorphic deep learning machines"
  Abstract: An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

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 "a supervised learning algorithm for multilayer spiking neural networks based on temporal coding toward energy efficient vlsi processor design"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "an energy efficient time domain analog vlsi neural network processor based on a pulse width modulation approach"
    - "supervised learning in spiking neural networks with phase change memory synapses"
    - "a time domain analog weighted sum calculation model for extremely low power vlsi implementation of multi layer neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"

Connected through hyperedge HE-3 (induced by source paper "event based vision a survey"):
  with members:
    - "event based motion segmentation by motion compensation"
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "atis spinnaker a fully event based visual tracking demonstration"
    - "ced color event camera dataset"
    - "unsupervised learning of a hierarchical spiking neural network for optical flow estimation from events to global motion perception"
    - "high speed and high dynamic range video with an event camera"
    - "end to end learning of representations for asynchronous event based data"
    - "speed invariant time surface for learning to detect corner points with event based cameras"

Connected through hyperedge HE-4 (induced by source paper "deep learning in spiking neural networks"):
  with members:
    - "representation learning using event based stdp"
    - "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation"
    - "optimal localist and distributed coding of spatiotemporal spike patterns through stdp and coincidence detection"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "bp stdp approximating backpropagation using spike timing dependent plasticity"

Connected through hyperedge HE-5 (induced by source paper "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"):
  with members:
    - "recognizing images with at most one spike per neuron"
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "fast and deep neuromorphic learning with time to first spike coding"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"

Connected through hyperedge HE-6 (induced by source paper "enabling spike based backpropagation in state of the art deep neural network architectures"):
  with members:
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "direct training for spiking neural networks faster larger better"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "slayer spike layer error reassignment in time"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-7 (induced by source paper "biologically plausible deep learning but how far can we go with shallow networks"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "deep learning in spiking neural networks"
    - "putting a bug in ml the moth olfactory network learns to read mnist"
    - "error forward propagation reusing feedforward connections to propagate errors in deep learning"
    - "feedback alignment in deep convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "a biologically plausible learning rule for deep learning in the brain"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-8 (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"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"

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)