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 "gradient ell_1 regularization for quantization robustness"):
  Abstract: We analyze the effect of quantizing weights and activations of neural networks on their loss and derive a simple regularization scheme that improves robustness against post-training quantization. By training quantization-ready networks, our approach enables storing a single set of weights that can be quantized on-demand to different bit-widths as energy and memory requirements of the application change. Unlike quantization-aware training using the straight-through estimator that only targets a specific bit-width and requires access to training data and pipeline, our regularization-based method paves the way for "on the fly'' post-training quantization to various bit-widths. We show that by modeling quantization as a $\ell_\infty$-bounded perturbation, the first-order term in the loss expansion can be regularized using the $\ell_1$-norm of gradients. We experimentally validate the effectiveness of our regularization scheme on different architectures on CIFAR-10 and ImageNet datasets.

Includes members:
  Member "data free quantization through weight equalization and bias correction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "post training piecewise linear quantization for deep neural networks")
  Member "improving neural network quantization without retraining using outlier channel splitting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse weight activation training")
  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 "same same but different recovering neural network quantization error through weight factorization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zeroq a novel zero shot quantization framework")
  Member "defensive quantization when efficiency meets robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "triple wins boosting accuracy robustness and efficiency together by enabling input adaptive inference")
  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")
  Member "sparse dnns with improved adversarial robustness":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on pruning adversarially robust neural networks")
  Member "the singular values of convolutional layers":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding generalization in deep learning via tensor methods")

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)