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: "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
  Abstract: Nanoscale resistive memory devices are expected to fuel dense integration of electronic synapses for large-scale neuromorphic systems. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs    in situ   learning and computing while driving a large number of resistive synapses is desired. This brief presents a novel leaky integrate-and-fire neuron design that implements the dual-mode operation of current integration and synaptic drive, with a single operational amplifier (opamp) and enables    in situ   learning with crossbar resistive synapses. The proposed design was implemented in a 0.18-    $\mu\mbox{m}$   CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses and demonstrate    in situ   associative learning. The neuron circuit occupies a small area of 0.01 mm 2 and has an energy efficiency value of 9.3 pJ/spike/synapse.

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 "sbsnn stochastic bits enabled binary spiking neural network with on chip learning for energy efficient neuromorphic computing at the edge"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "adam a method for stochastic optimization"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional spiking neural networks for image classification"):
  with members:
    - "deep learning in spiking neural networks"
    - "stimulating stdp to exploit locality for lifelong learning without catastrophic forgetting"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-4 (induced by source paper "efficient training and design of photonic neural network through neuroevolution"):
  with members:
    - "deep learning in spiking neural networks"
    - "design of optical neural networks with component imprecisions"
    - "all optical machine learning using diffractive deep neural networks"
    - "reinforcement learning in a large scale photonic recurrent neural network"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "end to end learning for self driving cars"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "deep learning in neural networks"

Connected through hyperedge HE-5 (induced by source paper "intelligent wireless sensor nodes for human footstep sound classification for security application"):
  with members:
    - "energy efficient cmos memristive synapses for mixed signal neuromorphic system on a chip"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-6 (induced by source paper "energy efficient cmos memristive synapses for mixed signal neuromorphic system on a chip"):
  with members:
    - "a compact cmos memristor emulator circuit and its applications"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-7 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"

Connected through hyperedge HE-8 (induced by source paper "a compact cmos memristor emulator circuit and its applications"):
  with members:
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

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)