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 cmos spiking neuron for dense memristor synapse connectivity for brain inspired computing"
  Abstract: Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have molded neural biophysical dynamics but are incompatible with memristor synapses, or used extra training circuitry thus eliminating much of the density advantages gained by using memristors, or were energy inefficient. Here we describe a novel CMOS spiking leaky integrate-and-fire neuron circuit. Building on a reconfigurable architecture with a single opamp, the described neuron accommodates a large number of memristor synapses, and enables online spike timing dependent plasticity (STDP) learning with optimized power consumption. Simulation results of an 180nm CMOS design showed 97% power efficiency metric when realizing STDP learning in 10,000 memristor synapses with a nominal 1M{\Omega} memristance, and only 13{\mu}A current consumption when integrating input spikes. Therefore, the described CMOS neuron contributes a generalized building block for large-scale brain-inspired neuromorphic systems.

Connected through hyperedge HE-1 (induced by source paper "neuromemristive circuits for edge computing a review"):
  with members:
    - "long short term memory networks in memristor crossbar arrays"
    - "learning in memristive neural network architectures using analog backpropagation circuits"
    - "neuron inspired data encoding memristive multi level memory cell"
    - "hierarchical temporal memory using memristor networks a survey"
    - "binary weighted memristive analog deep neural network for near sensor edge processing"
    - "design of cmos memristor circuits for lstm architecture"
    - "memristive lstm network hardware architecture for time series predictive modeling problem"
    - "approximate probabilistic neural networks with gated threshold logic"

Connected through hyperedge HE-2 (induced by source paper "dendritic inspired processing enables bio plausible stdp in compound binary synapses"):
  with members:
    - "bio inspired spiking convolutional neural network using layer wise sparse coding and stdp learning"
    - "training deep spiking neural networks using backpropagation"
    - "an online structural plasticity rule for generating better reservoirs"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "spin orbit torque induced spike timing dependent plasticity"
    - "homogeneous spiking neuromorphic system for real world pattern recognition"

Connected through hyperedge HE-3 (induced by source paper "a survey of neuromorphic computing and neural networks in hardware"):
  with members:
    - "a study of complex deep learning networks on high performance neuromorphic and quantum computers"
    - "probabilistic deep spiking neural systems enabled by magnetic tunnel junction"
    - "hybrid spintronic cmos spiking neural network with on chip learning devices circuits and systems"
    - "unsupervised learning in neuromemristive systems"
    - "a nonparametric framework for quantifying generative inference on neuromorphic systems"
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a new learning method for inference accuracy core occupation and performance co optimization on truenorth chip"
    - "exploiting the short term to long term plasticity transition in memristive nanodevice learning architectures"

Connected through hyperedge HE-4 (induced by source paper "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"):
  with members:
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "device and system level design considerations for analog non volatile memory based neuromorphic architectures"
    - "homogeneous spiking neuromorphic system for real world pattern 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)