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: "deep counter networks for asynchronous event based processing"
  Abstract: Despite their advantages in terms of computational resources, latency, and power consumption, event-based implementations of neural networks have not been able to achieve the same performance figures as their equivalent state-of-the-art deep network models. We propose counter neurons as minimal spiking neuron models which only require addition and comparison operations, thus avoiding costly multiplications. We show how inference carried out in deep counter networks converges to the same accuracy levels as are achieved with state-of-the-art conventional networks. As their event-based style of computation leads to reduced latency and sparse updates, counter networks are ideally suited for efficient compact and low-power hardware implementation. We present theory and training methods for counter networks, and demonstrate on the MNIST benchmark that counter networks converge quickly, both in terms of time and number of operations required, to state-of-the-art classification accuracy.

Connected through hyperedge HE-1 (induced by source paper "spikegrad an ann equivalent computation model for implementing backpropagation with spikes"):
  with members:
    - "deep spiking neural network with spike count based learning rule"
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep learning in spiking neural networks"
    - "training deep neural networks for binary communication with the whetstone method"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "direct training for spiking neural networks faster larger better"

Connected through hyperedge HE-2 (induced by source paper "low memory convolutional neural networks through incremental depth first processing"):
  with members:
    - "brainslug transparent acceleration of deep learning through depth first parallelism"
    - "universal deep neural network compression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mec memory efficient convolution for deep neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "compression aware training of deep networks"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-3 (induced by source paper "temporally efficient deep learning with spikes"):
  with members:
    - "deep spiking networks"
    - "sigma delta quantized networks"
    - "fast and efficient asynchronous neural computation with adapting spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "an objective function for stdp"

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)