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: "device and system level design considerations for analog non volatile memory based neuromorphic architectures"
  Abstract: This paper gives an overview of recent progress in the brain-inspired computing field with a focus on implementation using emerging memories as electronic synapses. Design considerations and challenges such as requirements and design targets on multilevel states, device variability, programming energy, array-level connectivity, fan-in/fan-out, wire energy, and IR drop are presented. Wires are increasingly important in design decisions, especially for large systems; and cycle-to-cycle variations have large impact on learning performance.

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"
    - "a simple and efficient snn and its performance robustness evaluation method to enable hardware implementation"
    - "redesigning commercial floating gate memory for analog computing applications"

Connected through hyperedge HE-2 (induced by source paper "high accuracy inference in neuromorphic circuits using hardware aware training"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "gxnor net training deep neural networks with ternary weights and activations without full precision memory under a unified discretization framework"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"):
  with members:
    - "deep residual learning for image recognition"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-4 (induced by source paper "magnetic skyrmion based synaptic devices"):
  with members:
    - "short term plasticity and long term potentiation in magnetic tunnel junctions towards volatile synapses"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "spin orbit torque induced spike timing dependent plasticity"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-5 (induced by source paper "a multi bit neuromorphic weight cell using ferroelectric fets suitable for soc integration"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "training a probabilistic graphical model with resistive switching electronic synapses"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"
    - "difference target propagation"
    - "event driven contrastive divergence for spiking neuromorphic systems"
    - "brain like associative learning using a nanoscale non volatile phase change synaptic device array"
    - "representation learning a review and new perspectives"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-7 (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 dense memristor synapse connectivity for brain inspired computing"
    - "a cmos spiking neuron for brain inspired neural networks with resistive synapses and in situ learning"
    - "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)