human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "l1 norm batch normalization for efficient training of deep neural networks"
  Abstract: Batch normalization (BN) has recently become a standard component for accelerating and improving the training of deep neural networks (DNNs). However, BN brings in additional calculations, consumes more memory, and significantly slows down the training iteration. Furthermore, the nonlinear square and sqrt operations in the normalization process impede low bit-width quantization techniques, which draw much attention to the deep learning hardware community. In this paper, we propose an    $L1$   -norm BN (L1BN) with only linear operations in both forward and backward propagations during training. L1BN is approximately equivalent to the conventional    $L2$   -norm BN (L2BN) by multiplying a scaling factor that equals    $({\pi }/{2})^{1/2}$   . Experiments on various convolutional neural networks and generative adversarial networks reveal that L1BN can maintain the same performance and convergence rate as L2BN but with higher computational efficiency. In real application-specified integrated circuit synthesis with reduced resources, L1BN achieves 25% speedup and 37% energy saving compared to the original L2BN. Our hardware-friendly normalization method not only surpasses L2BN in speed but also simplifies the design of deep learning accelerators. Last but not least, L1BN promises a fully quantized training of DNNs, which empowers future artificial intelligence applications on mobile devices with transfer and continual learning capability.

Connected through hyperedge HE-1 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks"):
  with members:
    - "a mean field theory of batch normalization"
    - "fixup initialization residual learning without normalization"
    - "weight standardization"
    - "a quantization friendly separable convolution for mobilenets"
    - "group normalization"
    - "spectrum concentration in deep residual learning a free probability approach"
    - "stable architectures for deep neural networks"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-2 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "proximal backpropagation"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-3 (induced by source paper "understanding batch normalization"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "three factors influencing minima in sgd"
    - "revisiting small batch training for deep neural networks"
    - "self normalizing neural networks"
    - "don t decay the learning rate increase the batch size"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "super convergence very fast training of residual networks using large learning rates"
    - "depth creates no bad local minima"

Connected through hyperedge HE-4 (induced by source paper "batch normalization sampling"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "group normalization"
    - "structurally sparsified backward propagation for faster long short term memory training"
    - "understanding batch normalization"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "a novel channel pruning method for deep neural network compression"
    - "imagenet training in minutes"

Connected through hyperedge HE-5 (induced by source paper "direct training for spiking neural networks faster larger better"):
  with members:
    - "deep learning in spiking neural networks"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "is neuromorphic mnist neuromorphic analyzing the discriminative power of neuromorphic datasets in the time domain"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "spiking deep residual network"
    - "hats histograms of averaged time surfaces for robust event based object classification"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "spatio temporal backpropagation for training high performance spiking neural networks"

Connected through hyperedge HE-6 (induced by source paper "scalable methods for 8 bit training of neural networks"):
  with members:
    - "mixed precision training of convolutional neural networks using integer operations"
    - "training and inference with integers in deep neural networks"
    - "wrpn wide reduced precision networks"
    - "the high dimensional geometry of binary neural networks"
    - "towards accurate binary convolutional neural network"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "convolutional neural networks using logarithmic data representation"

Question: Please tell me which class the center node 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)