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: "quantization and training of neural networks for efficient integer arithmetic only inference"
  Abstract: The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a quantization scheme that allows inference to be carried out using integer-only arithmetic, which can be implemented more efficiently than floating point inference on commonly available integer-only hardware. We also co-design a training procedure to preserve end-to-end model accuracy post quantization. As a result, the proposed quantization scheme improves the tradeoff between accuracy and on-device latency. The improvements are significant even on MobileNets, a model family known for run-time efficiency, and are demonstrated in ImageNet classification and COCO detection on popular CPUs.

Connected through hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "bidet an efficient binarized object detector"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "thundernet towards real time generic object detection"
    - "embarrassingly simple binary representation learning"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "importance estimation for neural network pruning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "efficient neural network compression"

Connected through hyperedge HE-3 (induced by source paper "fractional skipping towards finer grained dynamic cnn inference"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "slimmable neural networks"
    - "hybrid pruning thinner sparse networks for fast inference on edge devices"
    - "deep k means re training and parameter sharing with harder cluster assignments for compressing deep convolutions"
    - "haq hardware aware automated quantization with mixed precision"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "skipnet learning dynamic routing in convolutional networks"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-4 (induced by source paper "a simplified fully quantized transformer for end to end speech recognition"):
  with members:
    - "q8bert quantized 8bit bert"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "a comparative study on transformer vs rnn in speech applications"
    - "language modeling with deep transformers"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "fully quantized transformer for improved translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-5 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks"):
  with members:
    - "an optimized recurrent unit for ultra low power keyword spotting"
    - "streaming end to end speech recognition for mobile devices"
    - "donut ctc based query by example keyword spotting"
    - "efficient keyword spotting using dilated convolutions and gating"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "automatic grammar augmentation for robust voice command recognition"
    - "pyroomacoustics a python package for audio room simulation and array processing algorithms"
    - "spoken language understanding on the edge"

Connected through hyperedge HE-6 (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"
    - "minimum energy quantized neural networks"

Connected through hyperedge HE-7 (induced by source paper "robust quantization one model to rule them all"):
  with members:
    - "bit efficient quantization for deep neural networks"
    - "gdrq group based distribution reshaping for quantization"
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "low bit quantization of neural networks for efficient inference"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "neural network distiller a python package for dnn compression research"
    - "sinreq generalized sinusoidal regularization for low bitwidth deep quantized training"
    - "fighting quantization bias with bias"

Connected through hyperedge HE-8 (induced by source paper "lance efficient low precision quantized winograd convolution for neural networks based on graphics processing units"):
  with members:
    - "jointly sparse convolutional neural networks in dual spatial winograd domains"
    - "efficient winograd or cook toom convolution kernel implementation on widely used mobile cpus"
    - "a survey on methods and theories of quantized neural networks"
    - "recent advances in efficient computation of deep convolutional neural networks"
    - "spatial winograd pruning enabling sparse winograd convolution"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "ternary weight networks"
    - "quantized neural networks training neural networks with low precision weights and activations"

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)