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: "acquisition of visual features through probabilistic spike timing dependent plasticity"
  Abstract: This paper explores modifications to a feedforward five-layer spiking convolutional network (SCN) of the ventral visual stream [Masquelier, T., Thorpe, S., Unsupervised learning of visual features through spike timing dependent plasticity. PLoS Computational Biology, 3(2), 247-257]. The original model showed that a spike-timing-dependent plasticity (STDP) learning algorithm embedded in an appropriately selected SCN could perform unsupervised feature discovery. The discovered features where interpretable and could effectively be used to perform rapid binary decisions in a classifier. In order to study the robustness of the previous results, the present research examines the effects of modifying some of the components of the original model. For improved biological realism, we replace the original non-leaky integrate-and-fire neurons with Izhikevich-like neurons. We also replace the original STDP rule with a novel rule that has a probabilistic interpretation. The probabilistic STDP slightly but significantly improves the performance for both types of model neurons. Use of the Izhikevich-like neuron was not found to improve performance although performance was still comparable to the IF neuron. This shows that the model is robust enough to handle more biologically realistic neurons. We also conclude that the underlying reasons for stable performance in the model are preserved despite the overt changes to the explicit components of the model.

Connected through hyperedge HE-1 (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"
    - "event driven random back propagation enabling neuromorphic deep learning machines"

Connected through hyperedge HE-2 (induced by source paper "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"):
  with members:
    - "deep convolutional spiking neural networks for image classification"
    - "deep learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "online learning a comprehensive survey"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning visual features under motion invariance"):
  with members:
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "cognitive action laws the case of visual features"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "learning features by watching objects move"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised learning of edges"
    - "unsupervised learning of visual representations using videos"
    - "unsupervised learning of spatiotemporally coherent metrics"

Connected through hyperedge HE-4 (induced by source paper "representation learning using event based stdp"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "a spiking network that learns to extract spike signatures from speech signals"):
  with members:
    - "training a hidden markov model with a bayesian spiking neural network"

Connected through hyperedge HE-6 (induced by source paper "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "a spiking network that learns to extract spike signatures from speech signals"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training a hidden markov model with a bayesian spiking neural network"
    - "training deep spiking neural networks using backpropagation"
    - "deep learning in neural networks"
    - "event driven contrastive divergence for spiking neuromorphic systems"

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)