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: "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
  Abstract: Spiking Neural Networks (SNN) are more closely related to brain-like computation and inspire hardware implementation. This is enabled by small networks that give high performance on standard classification problems. In literature, typical SNNs are deep and complex in terms of network structure, weight update rules and learning algorithms. This makes it difficult to translate them into hardware. In this paper, we first develop a simple 2-layered network in software which compares with the state of the art on four different standard data-sets within SNNs and has improved efficiency. For example, it uses lower number of neurons (3 x), synapses (3.5 x) and epochs for training (30 x) for the Fisher Iris classification problem. The efficient network is based on effective population coding and synapse-neuron co-design. Second, we develop a computationally efficient (15000 x) and accurate (correlation of 0.98) method to evaluate the performance of the network without standard recognition tests. Third, we show that the method produces a robustness metric that can be used to evaluate noise tolerance.

Connected through hyperedge HE-1 (induced by source paper "ultra low energy charge trap flash based synapse enabled by parasitic leakage mitigation"):
  with members:
    - "an on chip trainable and the clock less spiking neural network with 1r memristive synapses"
    - "a case for multiple and parallel rrams as synaptic model for training snns"
    - "a software equivalent snn hardware using rram array for asynchronous real time learning"
    - "device and system level design considerations for analog non volatile memory based neuromorphic architectures"
    - "redesigning commercial floating gate memory for analog computing applications"

Connected through hyperedge HE-2 (induced by source paper "on chip learning in a conventional silicon mosfet based analog hardware neural network"):
  with members:
    - "analog cmos based resistive processing unit for deep neural network training"

Connected through hyperedge HE-3 (induced by source paper "on the algorithmic power of spiking neural networks"):
  with members:
    - "a basic compositional model for spiking neural networks"
    - "dictionary learning by dynamical neural networks"
    - "neuro ram unit with applications to similarity testing and compression in spiking neural networks"
    - "sparse coding by spiking neural networks convergence theory and computational results"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "an event based architecture for solving constraint satisfaction problems"
    - "spiking analog vlsi neuron assemblies as constraint satisfaction problem solvers"
    - "learning precise spike train to spike train transformations in multilayer feedforward neuronal networks"

Connected through hyperedge HE-4 (induced by source paper "an on chip trainable and the clock less spiking neural network with 1r memristive synapses"):
  with members:
    - "a software equivalent snn hardware using rram array for asynchronous real time learning"
    - "training deep spiking neural networks using backpropagation"
    - "spiking deep networks with lif neurons"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"

Connected through hyperedge HE-5 (induced by source paper "a case for multiple and parallel rrams as synaptic model for training snns"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"

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)