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: "rounding methods for neural networks with low resolution synaptic weights"
  Abstract: Neural network algorithms simulated on standard computing platforms typically make use of high resolution weights, with floating-point notation. However, for dedicated hardware implementations of such algorithms, fixed-point synaptic weights with low resolution are preferable. The basic approach of reducing the resolution of the weights in these algorithms by standard rounding methods incurs drastic losses in performance. To reduce the resolution further, in the extreme case even to binary weights, more advanced techniques are necessary. To this end, we propose two methods for mapping neural network algorithms with high resolution weights to corresponding algorithms that work with low resolution weights and demonstrate that their performance is substantially better than standard rounding. We further use these methods to investigate the performance of three common neural network algorithms under fixed memory size of the weight matrix with different weight resolutions. We show that dedicated hardware systems, whose technology dictates very low weight resolutions (be they electronic or biological) could in principle implement the algorithms we study.

Connected through hyperedge HE-1 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural ordinary differential equations"):
  with members:
    - "training lstm networks with resistive cross point devices"
    - "rethinking floating point for deep learning"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-2 (induced by source paper "a camera that cnns towards embedded neural networks on pixel processor arrays"):
  with members:
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "ternary weight networks"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "trained ternary quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "neural networks with few multiplications"

Connected through hyperedge HE-3 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural odes"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "mixed precision training"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-4 (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"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"

Connected through hyperedge HE-5 (induced by source paper "neural and synaptic array transceiver a brain inspired computing framework for embedded learning"):
  with members:
    - "contrastive hebbian learning with random feedback weights"
    - "membrane dependent neuromorphic learning rule for unsupervised spike pattern detection"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "learning in the machine random backpropagation and the deep learning channel"
    - "forward table based presynaptic event triggered spike timing dependent plasticity"
    - "stochastic synapses enable efficient brain inspired learning machines"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "convolutional networks for fast energy efficient neuromorphic computing"

Connected through hyperedge HE-6 (induced by source paper "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "adaption toolbox and benchmark for training convolutional neural networks with reduced numerical precision weights and activation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "delta networks for optimized recurrent network computation"
    - "hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "mixed precision training of deep neural networks using computational memory"):
  with members:
    - "mixed precision in memory computing"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-8 (induced by source paper "rethinking numerical representations for deep neural networks"):
  with members:
    - "perforatedcnns acceleration through elimination of redundant convolutions"
    - "compressing neural networks with the hashing trick"
    - "very deep convolutional networks for large scale image recognition"
    - "deep speech scaling up end to end speech recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "low precision arithmetic for deep learning"

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)