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: "network plasticity as bayesian inference"
  Abstract: General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing. We propose that inherently stochastic features of synaptic plasticity and spine motility enable cortical networks of neurons to carry out probabilistic inference by sampling from a posterior distribution of network configurations. This model provides a viable alternative to existing models that propose convergence of parameters to maximum likelihood values. It explains how priors on weight distributions and connection probabilities can be merged optimally with learned experience, how cortical networks can generalize learned information so well to novel experiences, and how they can compensate continuously for unforeseen disturbances of the network. The resulting new theory of network plasticity explains from a functional perspective a number of experimental data on stochastic aspects of synaptic plasticity that previously appeared to be quite puzzling.

Connected through hyperedge HE-1 (induced by source paper "spiking neural networks for inference and learning a memristor based design perspective"):
  with members:
    - "on resistive memories one step row readout technique and sensing circuitry"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "surrogate gradient learning in spiking neural networks"
    - "contrastive hebbian learning with random feedback weights"
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "slayer spike layer error reassignment in time"
    - "deep supervised learning using local errors"

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 "embodied synaptic plasticity with online reinforcement learning"):
  with members:
    - "synaptic plasticity dynamics for deep continuous local learning decolle"
    - "synaptic plasticity dynamics for deep continuous local learning"
    - "long short term memory and learning to learn in networks of spiking neurons"
    - "deep rewiring training very sparse deep networks"
    - "proximal policy optimization algorithms"
    - "hamiltonian synaptic sampling in a model for reward gated network plasticity"
    - "asynchronous methods for deep reinforcement learning"
    - "camkii activation supports reward based neural network optimization through hamiltonian sampling"

Connected through hyperedge HE-4 (induced by source paper "long short term memory and learning to learn in networks of spiking neurons"):
  with members:
    - "on the convergence of adam and beyond"
    - "meta reinforcement learning of structured exploration strategies"
    - "deep rewiring training very sparse deep networks"
    - "proximal policy optimization algorithms"
    - "learning to reinforcement learn"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "training dynamic exponential family models with causal and lateral dependencies for generalized neuromorphic computing"):
  with members:
    - "spatio temporal backpropagation for training high performance spiking neural networks"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "a brief introduction to machine learning for engineers"
    - "supervised learning in spiking neural networks for precise temporal encoding"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-6 (induced by source paper "control of the correlation of spontaneous neuron activity in biological and noise activated cmos artificial neural microcircuits"):
  with members:
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-7 (induced by source paper "stochastic synapses enable efficient brain inspired learning machines"):
  with members:
    - "real time unsupervised learning of visual stimuli in neuromorphic vlsi systems"
    - "gibbs sampling with low power spiking digital neurons"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-8 (induced by source paper "hamiltonian synaptic sampling in a model for reward gated network plasticity"):
  with members:
    - "equilibrated adaptive learning rates for non convex optimization"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"

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)