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: "large scale neuromorphic spiking array processors a quest to mimic the brain"
  Abstract: Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems. The brain has evolved over billions of years to solve difficult engineering problems by using efficient, parallel, low-power computation. The goal of NE is to design systems capable of brain-like computation. Numerous large-scale neuromorphic projects have emerged recently. This interdisciplinary field was listed among the top 10 technology breakthroughs of 2014 by the MIT Technology Review and among the top 10 emerging technologies of 2015 by the World Economic Forum. NE has two-way goals: one, a scientific goal to understand the computational properties of biological neural systems by using models implemented in integrated circuits (ICs); second, an engineering goal to exploit the known properties of biological systems to design and implement efficient devices for engineering applications. Building hardware neural emulators can be extremely useful for simulating large-scale neural models to explain how intelligent behavior arises in the brain. The principle advantages of neuromorphic emulators are that they are highly energy efficient, parallel and distributed, and require a small silicon area. Thus, compared to conventional CPUs, these neuromorphic emulators are beneficial in many engineering applications such as for the porting of deep learning algorithms for various recognitions tasks. In this review article, we describe some of the most significant neuromorphic spiking emulators, compare the different architectures and approaches used by them, illustrate their advantages and drawbacks, and highlight the capabilities that each can deliver to neural modelers.

Connected through hyperedge HE-1 (induced by source paper "a neuromorphic proto object based dynamic visual saliency model with an fpga implementation"):
  with members:
    - "spatio temporal saliency networks for dynamic saliency prediction"

Connected through hyperedge HE-2 (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-3 (induced by source paper "optimizing the energy consumption of spiking neural networks for neuromorphic applications"):
  with members:
    - "surrogate gradient learning in spiking neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "a survey on methods and theories of quantized neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "slayer spike layer error reassignment in time"
    - "unsupervised regenerative learning of hierarchical features in spiking deep networks for object recognition"
    - "pruning convolutional neural networks for resource efficient inference"

Connected through hyperedge HE-4 (induced by source paper "benchmarking physical performance of neural inference circuits"):
  with members:
    - "enabling spike based backpropagation in state of the art deep neural network architectures"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "benchmarking inverse rashba edelstein magnetoelectric devices for neuromorphic computing"
    - "benchmarking keyword spotting efficiency on neuromorphic hardware"
    - "an analysis of deep neural network models for practical applications"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "origami a 803 gop s w convolutional network accelerator"

Connected through hyperedge HE-5 (induced by source paper "efficient reward based structural plasticity on a spinnaker 2 prototype"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "an fpga based massively parallel neuromorphic cortex simulator"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "neuromorphic hardware in the loop training a deep spiking network on the brainscales wafer scale system"
    - "learning both weights and connections for efficient neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-6 (induced by source paper "simulation of neural function in an artificial hebbian network"):
  with members:
    - "demonstrating advantages of neuromorphic computation a pilot study"
    - "efficient reward based structural plasticity on a spinnaker 2 prototype"
    - "the symbol grounding problem"

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)