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: "homogeneous spiking neuromorphic system for real world pattern recognition"
  Abstract: A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.

Connected through hyperedge HE-1 (induced by source paper "a deep unsupervised feature learning spiking neural network with binarized classification layers for emnist classification"):
  with members:
    - "a spiking network for inference of relations trained with neuromorphic backpropagation"
    - "deep convolutional spiking neural networks for image classification"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "s4nn temporal backpropagation for spiking neural networks with one spike per neuron"
    - "towards scalable efficient and accurate deep spiking neural networks with backward residual connections stochastic softmax and hybridization"
    - "spyketorch efficient simulation of convolutional spiking neural networks with at most one spike per neuron"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"

Connected through hyperedge HE-2 (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-3 (induced by source paper "deep convolutional spiking neural networks for image classification"):
  with members:
    - "deep learning in spiking neural networks"
    - "stimulating stdp to exploit locality for lifelong learning without catastrophic forgetting"
    - "dendritic inspired processing enables bio plausible stdp in compound binary synapses"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "bio inspired unsupervised learning of visual features leads to robust invariant object recognition"
    - "training deep spiking neural networks using backpropagation"

Connected through hyperedge HE-4 (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"
    - "a cmos spiking neuron for dense memristor synapse connectivity for brain inspired computing"
    - "spin orbit torque induced spike timing dependent plasticity"

Connected through hyperedge HE-5 (induced by source paper "energy efficient cmos memristive synapses for mixed signal neuromorphic system on a chip"):
  with members:
    - "a compact cmos memristor emulator circuit and its applications"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

Connected through hyperedge HE-6 (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"
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
    - "training deep spiking neural networks using backpropagation"
    - "spiking deep networks with lif neurons"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a compact cmos memristor emulator circuit and its applications"):
  with members:
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "enabling bio plausible multi level stdp using cmos neurons with dendrites and bistable rrams"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "integration of nanoscale memristor synapses in neuromorphic computing architectures"

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)