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: "fast and efficient asynchronous neural computation with adapting spiking neural networks"
  Abstract: textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It#R##N#is an open question how real spiking neurons produce the kind of powerful#R##N#neural computation that is possible with deep artificial neural networks, using#R##N#only so very few spikes to communicate. Building on recent insights in#R##N#neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on#R##N#adaptive spiking neurons. These spiking neurons efficiently encode information#R##N#in spike-trains using a form of Asynchronous Pulsed Sigma-Delta coding while#R##N#homeostatically optimizing their firing rate. In the proposed paradigm of#R##N#spiking neuron computation, neural adaptation is tightly coupled to synaptic#R##N#plasticity, to ensure that downstream neurons can correctly decode upstream#R##N#spiking neurons. We show that this type of network is inherently able to carry#R##N#out asynchronous and event-driven neural computation, while performing#R##N#identical to corresponding artificial neural networks (ANNs). In particular, we#R##N#show that these adaptive spiking neurons can be drop in replacements for ReLU#R##N#neurons in standard feedforward ANNs comprised of such units. We demonstrate#R##N#that this can also be successfully applied to a ReLU based deep convolutional#R##N#neural network for classifying the MNIST dataset. The ASNN thus outperforms#R##N#current Spiking Neural Networks (SNNs) implementations, while responding (up#R##N#to) an order of magnitude faster and using an order of magnitude fewer spikes.#R##N#Additionally, in a streaming setting where frames are continuously classified,#R##N#we show that the ASNN requires substantially fewer network updates as compared#R##N#to the corresponding ANN.

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 "closing the accuracy gap in an event based visual recognition task"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "conversion of artificial recurrent neural networks to spiking neural networks for low power neuromorphic hardware"
    - "training deep spiking neural networks using backpropagation"

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

Connected through hyperedge HE-4 (induced by source paper "sigma delta quantized networks"):
  with members:
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "deep spiking networks"
    - "training deep spiking neural networks using backpropagation"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)