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: "locally connected spiking neural networks for unsupervised feature learning"
  Abstract: In recent years, Spiking Neural Networks (SNNs) have demonstrated great successes in completing various Machine Learning tasks. We introduce a method for learning image features by \textit{locally connected layers} in SNNs using spike-timing-dependent plasticity (STDP) rule. In our approach, sub-networks compete via competitive inhibitory interactions to learn features from different locations of the input space. These \textit{Locally-Connected SNNs} (LC-SNNs) manifest key topological features of the spatial interaction of biological neurons. We explore biologically inspired n-gram classification approach allowing parallel processing over various patches of the the image space. We report the classification accuracy of simple two-layer LC-SNNs on two image datasets, which match the state-of-art performance and are the first results to date. LC-SNNs have the advantage of fast convergence to a dataset representation, and they require fewer learnable parameters than other SNN approaches with unsupervised learning. Robustness tests demonstrate that LC-SNNs exhibit graceful degradation of performance despite the random deletion of large amounts of synapses and neurons.

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"
    - "deep learning in spiking neural networks"
    - "multi layered spiking neural network with target timestamp threshold adaptation and stdp"
    - "unsupervised learning with self organizing spiking neural networks"
    - "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 "biosnet a fast learning and high robustness unsupervised biomimetic spiking neural network"):
  with members:
    - "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"
    - "emnist an extension of mnist to handwritten letters"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "csnns unsupervised backpropagation free convolutional neural networks for representation learning"):
  with members:
    - "unsupervised deep learning by neighbourhood discovery"
    - "unsupervised learning by competing hidden units"
    - "large scale adversarial representation learning"
    - "sparsely activated networks"
    - "unsupervised discovery of parts structure and dynamics"
    - "evolving losses for unlabeled video representation learning"
    - "revisiting self supervised visual representation learning"
    - "unsupervised embedding learning via invariant and spreading instance feature"

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)