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: "stochastic synapses enable efficient brain inspired learning machines"
  Abstract: Recent studies have shown that synaptic unreliability is a robust and sufficient mechanism for inducing the stochasticity observed in cortex. Here, we introduce Synaptic Sampling Machines (S2Ms), a class of neural network models that uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised learning. Similar to the original formulation of Boltzmann machines, these models can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. Synaptic stochasticity plays the dual role of an efficient mechanism for sampling, and a regularizer during learning akin to DropConnect. A local synaptic plasticity rule implementing an event-driven form of contrastive divergence enables the learning of generative models in an on-line fashion. S2Ms perform equally well using discrete-timed artificial units (as in Hopfield networks) or continuous-timed leaky integrate and fire neurons. The learned representations are remarkably sparse and robust to reductions in bit precision and synapse pruning: removal of more than 75% of the weakest connections followed by cursory re-learning causes a negligible performance loss on benchmark classification tasks. The spiking neuron-based S2Ms outperform existing spike-based unsupervised learners, while potentially offering substantial advantages in terms of power and complexity, and are thus promising models for on-line learning in brain-inspired hardware.

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 "inherent weight normalization in stochastic neural networks"):
  with members:
    - "self normalizing neural networks"
    - "learning discrete weights using the local reparameterization trick"
    - "emnist an extension of mnist to handwritten letters"
    - "learning to learn by gradient descent by gradient descent"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "fast and deep neuromorphic learning with time to first spike coding"):
  with members:
    - "biologically plausible deep learning but how far can we go with shallow networks"
    - "deep spiking neural network with spike count based learning rule"
    - "biologically inspired alternatives to backpropagation through time for learning in recurrent neural nets"
    - "deep learning in spiking neural networks"
    - "spinnaker 2 a 10 million core processor system for brain simulation and machine learning"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"

Connected through hyperedge HE-4 (induced by source paper "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"):
  with members:
    - "neuromorphic computing with multi memristive synapses"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "a differential memristive synapse circuit for on line learning in neuromorphic computing systems"
    - "deep learning in neural networks"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-5 (induced by source paper "large scale neuromorphic spiking array processors a quest to mimic the brain"):
  with members:
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "training a probabilistic graphical model with resistive switching electronic synapses"
    - "a neuromorphic hardware architecture using the neural engineering framework for pattern recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "neuromorphic electronic circuits for building autonomous cognitive systems"

Connected through hyperedge HE-6 (induced by source paper "contrastive hebbian learning with random feedback weights"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "tensorflow a system for large scale machine learning"
    - "difference target propagation"
    - "event driven contrastive divergence for spiking neuromorphic systems"

Connected through hyperedge HE-7 (induced by source paper "memristor based synaptic sampling machines"):
  with members:
    - "the future of neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"):
  with members:
    - "contrastive hebbian learning with random feedback weights"
    - "membrane dependent neuromorphic learning rule for unsupervised spike pattern detection"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "forward table based presynaptic event triggered spike timing dependent plasticity"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "tensorflow a system for large scale machine learning"

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)