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: "unsupervised learning with self organizing spiking neural networks"
  Abstract: We present a system comprising a hybridization of self-organized map (SOM) properties with spiking neural networks (SNNs) that retain many of the features of SOMs. Networks are trained in an unsupervised manner to learn a self-organized lattice of filters via excitatory-inhibitory interactions among populations of neurons. We develop and test various inhibition strategies, such as growing with inter-neuron distance and two distinct levels of inhibition. The quality of the unsupervised learning algorithm is evaluated using examples with known labels. Several biologically-inspired classification tools are proposed and compared, including population-level confidence rating, and n-grams using spike motif algorithm. Using the optimal choice of parameters, our approach produces improvements over state-of-art spiking neural networks.

Connected through hyperedge HE-1 (induced by source paper "spiking inception module for multi layer unsupervised spiking neural networks"):
  with members:
    - "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"
    - "locally connected spiking neural networks for unsupervised feature learning"
    - "deep learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "asp learning to forget with adaptive synaptic plasticity in spiking neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "spiking neural predictive coding for continual learning from data streams"):
  with members:
    - "a biologically plausible supervised learning method for spiking neural networks using the symmetric stdp rule"
    - "unsupervised learning by competing hidden units"
    - "lifelong neural predictive coding sparsity yields less forgetting when learning cumulatively"
    - "bio inspired digit recognition using reward modulated spike timing dependent plasticity in deep convolutional networks"
    - "like a baby visually situated neural language acquisition"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "continual learning of recurrent neural networks by locally aligning distributed representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "locally connected spiking neural networks for unsupervised feature learning"):
  with members:
    - "deep learning in spiking neural networks"
    - "stdp learning of image patches with convolutional spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "quantifying translation invariance in convolutional neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "efficient computation in adaptive artificial spiking neural networks"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"

Connected through hyperedge HE-4 (induced by source paper "lattice map spiking neural networks lm snns for clustering and classifying image data"):
  with members:
    - "stdp learning of image patches with convolutional spiking neural networks"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "training deep spiking neural networks using backpropagation"
    - "deep learning in neural networks"

Connected through hyperedge HE-5 (induced by source paper "bindsnet a machine learning oriented spiking neural networks library in python"):
  with members:
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "deep spiking networks"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "spiking deep networks with lif neurons"
    - "towards biologically plausible deep learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed 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)