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 "loss aware post training quantization"):
  Abstract: Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for INT4 (or lower) but provide reasonable accuracy for INT8 (or above). In this work, we study the effect of quantization on the structure of the loss landscape. We show that the structure is flat and separable for mild quantization, enabling straightforward post-training quantization methods to achieve good results. On the other hand, we show that with more aggressive quantization, the loss landscape becomes highly non-separable with sharp minima points, making the selection of quantization parameters more challenging. Armed with this understanding, we design a method that quantizes the layer parameters jointly, enabling significant accuracy improvement over current post-training quantization methods. Reference implementation accompanies the paper at this https URL

Includes members:
  Member "binaryduo reducing gradient mismatch in binary activation network by coupling binary activations":
    also connected through other hyperedges:
      (none)
  Member "near lossless post training quantization of deep neural networks via a piecewise linear approximation":
    also connected through other hyperedges:
      (none)
  Member "bit efficient quantization for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spike in performance training hybrid spiking neural networks with quantized activation functions")
  Member "back to simplicity how to train accurate bnns from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bats binary architecture search")
  Member "low bit quantization of neural networks for efficient inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "fighting quantization bias with bias":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "towards efficient training for neural network quantization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adabits neural network quantization with adaptive bit widths")
  Member "quantizing deep convolutional networks for efficient inference a whitepaper":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "post training piecewise linear quantization for deep neural networks")

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)