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: "training deep neural networks with low precision multiplications"
  Abstract: Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxout networks) on three benchmark datasets: MNIST, CIFAR-10 and SVHN. They are trained with three distinct formats: floating point, fixed point and dynamic fixed point. For each of those datasets and for each of those formats, we assess the impact of the precision of the multiplications on the final error after training. We find that very low precision is sufficient not just for running trained networks but also for training them. For example, it is possible to train Maxout networks with 10 bits multiplications.

Connected through hyperedge HE-1 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "an energy efficient fpga based deconvolutional neural networks accelerator for single image super resolution"):
  with members:
    - "a design methodology for efficient implementation of deconvolutional neural networks on an fpga"
    - "maximizing cnn accelerator efficiency through resource partitioning"
    - "text attentional convolutional neural network for scene text detection"
    - "accelerating the super resolution convolutional neural network"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "fixed point quantization of deep convolutional networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep learning in memristive nanowire networks"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "attention is all you need"
    - "noisy activation functions"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "wide residual networks"
    - "exploring the limits of language modeling"
    - "quantized neural networks training neural networks with low precision weights and activations"

Connected through hyperedge HE-4 (induced by source paper "towards explainable bit error tolerance of resistive ram based binarized neural networks"):
  with members:
    - "outstanding bit error tolerance of resistive ram based binarized neural networks"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "bmxnet an open source binary neural network implementation based on mxnet"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-5 (induced by source paper "progressive stochastic binarization of deep networks"):
  with members:
    - "deep convolutional sum product networks for probabilistic image representations"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "probabilistic binary neural networks"
    - "tractable querying and learning in hybrid domains via sum product networks"
    - "learning to train a binary neural network"
    - "bnn improved binary network training"

Connected through hyperedge HE-6 (induced by source paper "cascaded projection end to end network compression and acceleration"):
  with members:
    - "learning to prune filters in convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "nvidia tensor core programmability performance precision"
    - "yolov3 an incremental improvement"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "nisp pruning networks using neuron importance score propagation"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-7 (induced by source paper "xnor net improved binary neural networks"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "t net parametrizing fully convolutional nets with a single high order tensor"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "training competitive binary neural networks from scratch"
    - "wrpn training and inference using wide reduced precision networks"

Connected through hyperedge HE-8 (induced by source paper "improved training of binary networks for human pose estimation and image recognition"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "quantized densely connected u nets for efficient landmark localization"
    - "multi scale structure aware network for human pose estimation"
    - "simple baselines for human pose estimation and tracking"
    - "learning feature pyramids for human pose estimation"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"

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.LG(Machine Learning)