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 "training deep neural networks with 8 bit floating point numbers"):
  Abstract: The state-of-the-art hardware platforms for training Deep Neural Networks (DNNs) are moving from traditional single precision (32-bit) computations towards 16 bits of precision -- in large part due to the high energy efficiency and smaller bit storage associated with using reduced-precision representations. However, unlike inference, training with numbers represented with less than 16 bits has been challenging due to the need to maintain fidelity of the gradient computations during back-propagation. Here we demonstrate, for the first time, the successful training of DNNs using 8-bit floating point numbers while fully maintaining the accuracy on a spectrum of Deep Learning models and datasets. In addition to reducing the data and computation precision to 8 bits, we also successfully reduce the arithmetic precision for additions (used in partial product accumulation and weight updates) from 32 bits to 16 bits through the introduction of a number of key ideas including chunk-based accumulation and floating point stochastic rounding. The use of these novel techniques lays the foundation for a new generation of hardware training platforms with the potential for 2-4x improved throughput over today's systems.

Includes members:
  Member "mixed precision training of convolutional neural networks using integer operations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse weight activation training")
  Member "training and inference with integers in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  Member "pact parameterized clipping activation for quantized neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "mixed precision training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "flexpoint an adaptive numerical format for efficient training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "training variance and performance evaluation of neural networks in speech":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adacomp adaptive residual gradient compression for data parallel distributed training")
  Member "the microsoft 2016 conversational speech recognition system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "accelerating rnn transducer inference via one step constrained beam search")
  Member "google s neural machine translation system bridging the gap between human and machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

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)