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: "a learning framework for winner take all networks with stochastic synapses"
  Abstract: Many recent generative models make use of neural networks to transform the probability distribution of a simple low-dimensional noise process into the complex distribution of the data. This raises the question of whether biological networks operate along similar principles to implement a probabilistic model of the environment through transformations of intrinsic noise processes. The intrinsic neural and synaptic noise processes in biological networks, however, are quite different from the noise processes used in current abstract generative networks. This, together with the discrete nature of spikes and local circuit interactions among the neurons, raises several difficulties when using recent generative modeling frameworks to train biologically motivated models. In this paper, we show that a biologically motivated model based on multi-layer winner-take-all (WTA) circuits and stochastic synapses admits an approximate analytical description. This allows us to use the proposed networks in a variational learning setting where stochastic backpropagation is used to optimize a lower bound on the data log likelihood, thereby learning a generative model of the data. We illustrate the generality of the proposed networks and learning technique by using them in a structured output prediction task, and in a semi-supervised learning task. Our results extend the domain of application of modern stochastic network architectures to networks where synaptic transmission failure is the principal noise mechanism.

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"

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)