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: "unsupervised learning in synaptic sampling 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 the Synaptic Sampling Machine (SSM), a stochastic neural network model that uses synaptic unreliability as a means to stochasticity for sampling. Synaptic unreliability plays the dual role of an efficient mechanism for sampling in neuromorphic hardware, and a regularizer during learning akin to DropConnect. Similar to the original formulation of Boltzmann machines, the SSM can be viewed as a stochastic counterpart of Hopfield networks, but where stochasticity is induced by a random mask over the connections. The SSM is trained to learn generative models with a synaptic plasticity rule implementing an event-driven form of contrastive divergence. We demonstrate this by learning a model of MNIST hand-written digit dataset, and by testing it in recognition and inference tasks. We find that SSMs outperform restricted Boltzmann machines (4.4% error rate vs. 5%), they are more robust to overfitting, and tend to learn sparser representations. SSMs are remarkably robust to weight pruning: removal of more than 80% of the weakest connections followed by cursory re-learning causes only a negligible performance loss on the MNIST task (4.8% error rate). These results show that SSMs offer substantial improvements in terms of performance, power and complexity over existing methods for unsupervised learning in spiking neural networks, and are thus promising models for machine learning in neuromorphic execution platforms.

Connected through hyperedge HE-1 (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"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-2 (induced by source paper "hardware efficient on line learning through pipelined truncated error backpropagation in binary state networks"):
  with members:
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "eie efficient inference engine on compressed deep neural network"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "trained ternary quantization"
    - "convolutional networks for fast energy efficient neuromorphic computing"

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)