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: "flexpoint an adaptive numerical format for efficient training of deep neural networks"
  Abstract: Deep neural networks are commonly developed and trained in 32-bit floating point format. Significant gains in performance and energy efficiency could be realized by training and inference in numerical formats optimized for deep learning. Despite advances in limited precision inference in recent years, training of neural networks in low bit-width remains a challenging problem. Here we present the Flexpoint data format, aiming at a complete replacement of 32-bit floating point format training and inference, designed to support modern deep network topologies without modifications. Flexpoint tensors have a shared exponent that is dynamically adjusted to minimize overflows and maximize available dynamic range. We validate Flexpoint by training AlexNet, a deep residual network and a generative adversarial network, using a simulator implemented with the neon deep learning framework. We demonstrate that 16-bit Flexpoint closely matches 32-bit floating point in training all three models, without any need for tuning of model hyperparameters. Our results suggest Flexpoint as a promising numerical format for future hardware for training and inference.

Connected through hyperedge HE-1 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "mixed precision training with 8 bit floating point"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "nvidia tensor core programmability performance precision"
    - "training and inference with integers in deep neural networks"
    - "rethinking floating point for deep learning"
    - "a survey on methods and theories of quantized neural networks"

Connected through hyperedge HE-2 (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"
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-3 (induced by source paper "qutibench benchmarking neural networks on heterogeneous hardware"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "binareye an always on energy accuracy scalable binary cnn processor with all memory on chip in 28nm cmos"
    - "training and inference with integers in deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "bismo a scalable bit serial matrix multiplication overlay for reconfigurable computing"
    - "tbd benchmarking and analyzing deep neural network training"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-4 (induced by source paper "towards unified int8 training for convolutional neural network"):
  with members:
    - "regularizing activation distribution for training binarized deep networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "on the convergence of adam and beyond"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "learned step size quantization"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"

Connected through hyperedge HE-5 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"

Connected through hyperedge HE-6 (induced by source paper "dsconv efficient convolution operator"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "training dnns with hybrid block floating point"

Connected through hyperedge HE-7 (induced by source paper "data free quantization through weight equalization and bias correction"):
  with members:
    - "equi normalization of neural networks"
    - "same same but different recovering neural network quantization error through weight factorization"
    - "fighting quantization bias with bias"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a quantization friendly separable convolution for mobilenets"
    - "probabilistic binary neural networks"
    - "relaxed quantization for discretized neural networks"

Connected through hyperedge HE-8 (induced by source paper "swalp stochastic weight averaging in low precision training"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "training deep neural networks with 8 bit floating point numbers"
    - "averaging weights leads to wider optima and better generalization"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "training quantized nets a deeper understanding"

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)