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: "minimum energy quantized neural networks"
  Abstract: This work targets the automated minimum-energy optimization of Quantized Neural Networks (QNNs) - networks using low precision weights and activations. These networks are trained from scratch at an arbitrary fixed point precision. At iso-accuracy, QNNs using fewer bits require deeper and wider network architectures than networks using higher precision operators, while they require less complex arithmetic and less bits per weights. This fundamental trade-off is analyzed and quantified to find the minimum energy QNN for any benchmark and hence optimize energy-efficiency. To this end, the energy consumption of inference is modeled for a generic hardware platform. This allows drawing several conclusions across different benchmarks. First, energy consumption varies orders of magnitude at iso-accuracy depending on the number of bits used in the QNN. Second, in a typical system, BinaryNets or int4 implementations lead to the minimum energy solution, outperforming int8 networks up to 2-10x at iso-accuracy. All code used for QNN training is available from this https URL

Connected through hyperedge HE-1 (induced by source paper "pulp nn accelerating quantized neural networks on parallel ultra low power risc v processors"):
  with members:
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"
    - "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"
    - "haq hardware aware automated quantization with mixed precision"
    - "neura ghe exploiting cpu fpga synergies for efficient and flexible cnn inference acceleration on zynq socs"
    - "pact parameterized clipping activation for quantized neural networks"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-2 (induced by source paper "compressing deep neural networks on fpgas to binary and ternary precision with hls4ml"):
  with members:
    - "deep learning with low precision by half wave gaussian quantization"
    - "finn a framework for fast scalable binarized neural network inference"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "morphic a 65 nm 738k synapse mm 2 quad core binary weight digital neuromorphic processor with stochastic spike driven online learning"):
  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"
    - "a neuromorphic systems approach to in memory computing with non ideal memristive devices from mitigation to exploitation"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"

Connected through hyperedge HE-4 (induced by source paper "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"):
  with members:
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "memory and information processing in neuromorphic systems"
    - "a biological realtime neuromorphic system in 28 nm cmos using low leakage switched capacitor circuits"
    - "switched capacitor realization of presynaptic short term plasticity and stop learning synapses in 28 nm cmos"
    - "biologically inspired spiking neurons piecewise linear models and digital implementation"

Connected through hyperedge HE-5 (induced by source paper "layerwise noise maximisation to train low energy deep neural networks"):
  with members:
    - "training modern deep neural networks for memory fault robustness"
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "training wide residual networks for deployment using a single bit for each weight"
    - "wide residual networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"
    - "the high dimensional geometry of binary neural networks"

Connected through hyperedge HE-7 (induced by source paper "xnor neural engine a hardware accelerator ip for 21 6 fj op binary neural network inference"):
  with members:
    - "xnorbin a 95 top s w hardware accelerator for binary convolutional neural networks"
    - "cmsis nn efficient neural network kernels for arm cortex m cpus"
    - "an iot endpoint system on chip for secure and energy efficient near sensor analytics"
    - "towards accurate binary convolutional neural network"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "design automation for binarized neural networks a quantum leap opportunity"

Connected through hyperedge HE-8 (induced by source paper "slim simultaneous logic in memory computing exploiting bilayer analog oxram devices"):
  with members:
    - "mitigating asymmetric nonlinear weight update effects in hardware neural network based on analog resistive synapse"
    - "x sram enabling in memory boolean computations in cmos static random access memories"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "memory and information processing in neuromorphic systems"
    - "hitting the memory wall implications of the obvious"

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)