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 "deep learning with limited numerical precision"):
  Abstract: Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computation on neural network training. Within the context of low-precision fixed-point computations, we observe the rounding scheme to play a crucial role in determining the network's behavior during training. Our results show that deep networks can be trained using only 16-bit wide fixed-point number representation when using stochastic rounding, and incur little to no degradation in the classification accuracy. We also demonstrate an energy-efficient hardware accelerator that implements low-precision fixed-point arithmetic with stochastic rounding.

Includes members:
  Member "deep image scaling up image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stochastic optimization of plain convolutional neural networks with simple methods")
  Member "learning machines implemented on non deterministic hardware":
    also connected through other hyperedges:
      (none)
  Member "training deep neural networks with low precision multiplications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources")
  Member "low precision arithmetic for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "elastic consistency a general consistency model for distributed stochastic gradient descent")
  Member "improving neural networks by preventing co adaptation of feature detectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "deep big simple neural nets for handwritten digit recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "accelerating scientific computations with mixed precision algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a note on solving nonlinear optimization problems in variable precision")

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)