human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks"):
  Abstract: Training with larger number of parameters while keeping fast iterations is an increasingly adopted strategy and trend for developing better performing Deep Neural Network (DNN) models. This necessitates increased memory footprint and computational requirements for training. Here we introduce a novel methodology for training deep neural networks using 8-bit floating point (FP8) numbers. Reduced bit precision allows for a larger effective memory and increased computational speed. We name this method Shifted and Squeezed FP8 (S2FP8). We show that, unlike previous 8-bit precision training methods, the proposed method works out-of-the-box for representative models: ResNet-50, Transformer and NCF. The method can maintain model accuracy without requiring fine-tuning loss scaling parameters or keeping certain layers in single precision. We introduce two learnable statistics of the DNN tensors - shifted and squeezed factors that are used to optimally adjust the range of the tensors in 8-bits, thus minimizing the loss in information due to quantization.

Includes members:
  Member "a study of bfloat16 for deep learning training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks")
  Member "mixed precision training with 8 bit floating point":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic rounding algorithms and hardware accelerator")
  Member "scalable methods for 8 bit training of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sparse weight activation training")
  Member "mixed precision training of convolutional neural networks using integer operations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "nvidia tensor core programmability performance precision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gpu tensor cores for fast arithmetic reductions")
  Member "training and inference with integers in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "rethinking floating point for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic rounding and reduced precision fixed point arithmetic for solving neural ordinary differential equations")
  Member "a survey on methods and theories of quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lance efficient low precision quantized winograd convolution for neural networks based on graphics processing units")

Question: Please tell me which class the source paper 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)