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: "learning spatiotemporally encoded pattern transformations in structured spiking neural networks"
  Abstract: Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

Connected through hyperedge HE-1 (induced by source paper "surrogate gradient learning in spiking neural networks"):
  with members:
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "deep networks incorporating spiking neural dynamics"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "slayer spike layer error reassignment in time"
    - "smplr deep smpl reverse for 3d human pose and shape recovery"
    - "temporally efficient deep learning with spikes"

Connected through hyperedge HE-2 (induced by source paper "an introduction to probabilistic spiking neural networks probabilistic models learning rules and applications"):
  with members:
    - "learning first to spike policies for neuromorphic control using policy gradients"
    - "surrogate gradient learning in spiking neural networks"
    - "low power neuromorphic hardware for signal processing applications"
    - "training dynamic exponential family models with causal and lateral dependencies for generalized neuromorphic computing"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "boltzmann machines for time series"

Connected through hyperedge HE-3 (induced by source paper "scienet deep learning with spike assisted contextual information extraction"):
  with members:
    - "mixture of pre processing experts model for noise robust deep learning on resource constrained platforms"
    - "deep learning in spiking neural networks"
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "supervised learning based on temporal coding in spiking neural networks"):
  with members:
    - "densely connected convolutional networks"
    - "training deep spiking neural networks using backpropagation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "do deep nets really need to be deep"
    - "theano new features and speed improvements"

Connected through hyperedge HE-5 (induced by source paper "an online structural plasticity rule for generating better reservoirs"):
  with members:
    - "learning spike time codes through morphological learning with binary synapses"
    - "an online unsupervised structural plasticity algorithm for spiking neural networks"
    - "liquid state machine with dendritically enhanced readout for low power neuromorphic vlsi implementations"
    - "hardware amenable structural learning for spike based pattern classification using a simple model of active dendrites"
    - "supervised learning in multilayer spiking neural networks"

Connected through hyperedge HE-6 (induced by source paper "supervised learning in spiking neural networks for precise temporal encoding"):
  with members:
    - "supervised learning in multilayer spiking neural networks"

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)