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: "a study of bfloat16 for deep learning training"
  Abstract: This paper presents the first comprehensive empirical study demonstrating the efficacy of the Brain Floating Point (BFLOAT16) half-precision format for Deep Learning training across image classification, speech recognition, language modeling, generative networks and industrial recommendation systems. BFLOAT16 is attractive for Deep Learning training for two reasons: the range of values it can represent is the same as that of IEEE 754 floating-point format (FP32) and conversion to/from FP32 is simple. Maintaining the same range as FP32 is important to ensure that no hyper-parameter tuning is required for convergence; e.g., IEEE 754 compliant half-precision floating point (FP16) requires hyper-parameter tuning. In this paper, we discuss the flow of tensors and various key operations in mixed precision training, and delve into details of operations, such as the rounding modes for converting FP32 tensors to BFLOAT16. We have implemented a method to emulate BFLOAT16 operations in Tensorflow, Caffe2, IntelCaffe, and Neon for our experiments. Our results show that deep learning training using BFLOAT16 tensors achieves the same state-of-the-art (SOTA) results across domains as FP32 tensors in the same number of iterations and with no changes to hyper-parameters.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "winograd convolution for dnns beyond linear polynomials"
    - "visual wake words dataset"
    - "sparse sparse architecture search for cnns on resource constrained microcontrollers"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "searching for mobilenetv3"
    - "efficient winograd convolution via integer arithmetic"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "neural networks on microcontrollers saving memory at inference via operator reordering"

Connected through hyperedge HE-2 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks"):
  with members:
    - "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"
    - "training deep neural networks with 8 bit floating point numbers"

Connected through hyperedge HE-3 (induced by source paper "meth a family of high resolution and variable shape image challenges"):
  with members:
    - "leveraging the bfloat16 artificial intelligence datatype for higher precision computations"
    - "continual lifelong learning with neural networks a review"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "exascale deep learning for climate analytics"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "online deep learning learning deep neural networks on the fly"

Connected through hyperedge HE-4 (induced by source paper "cheetah mixed low precision hardware software co design framework for dnns on the edge"):
  with members:
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-5 (induced by source paper "deep learning training on the edge with low precision posits"):
  with members:
    - "template based posit multiplication for training and inferring in neural networks"
    - "mixed precision training with 8 bit floating point"
    - "performance efficiency trade off of low precision numerical formats in deep neural networks"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "deep positron a deep neural network using the posit number system"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rethinking floating point for deep learning"

Connected through hyperedge HE-6 (induced by source paper "k tanh hardware efficient activations for deep learning"):
  with members:
    - "energy and policy considerations for deep learning in nlp"
    - "deep contextualized word representations"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "searching for activation functions"
    - "gaussian error linear units gelus"

Connected through hyperedge HE-7 (induced by source paper "panther a programmable architecture for neural network training harnessing energy efficient reram"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "training high performance and large scale deep neural networks with full 8 bit integers"
    - "training and inference with integers in deep neural networks"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "training deep neural networks with 8 bit floating point numbers"
    - "revisiting small batch training for deep neural networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mixed precision training"

Connected through hyperedge HE-8 (induced by source paper "adaptive loss scaling for mixed precision training"):
  with members:
    - "mixed precision training with 8 bit floating point"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "accumulation bit width scaling for ultra low precision training of deep networks"
    - "mixed precision training for nlp and speech recognition with openseq2seq"
    - "training deep neural networks with 8 bit floating point numbers"
    - "mixed precision training"
    - "ssd single shot multibox detector"
    - "deep residual learning for image 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.LG(Machine Learning)