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 "learn able parameter guided activation functions"):
  Abstract: In this paper, we explore the concept of adding learn-able slope and mean shift parameters to an activation function to improve the total response region. The characteristics of an activation function depend highly on the value of parameters. Making the parameters learn-able, makes the activation function more dynamic and capable to adapt as per the requirements of its neighboring layers. The introduced slope parameter is independent of other parameters in the activation function. The concept was applied to ReLU to develop Dual Line and DualParametric ReLU activation function. Evaluation on MNIST and CIFAR10 show that the proposed activation function Dual Line achieves top-5 position for mean accuracy among 43 activation functions tested with LENET4, LENET5, and WideResNet architectures. This is the first time more than 40 activation functions were analyzed on MNIST andCIFAR10 dataset at the same time. The study on the distribution of positive slope parameter beta indicates that the activation function adapts as per the requirements of the neighboring layers. The study shows that model performance increases with the proposed activation functions

Includes members:
  Member "improving deep learning by inverse square root linear units isrlus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "soft root sign activation function")
  Member "activation functions comparison of trends in practice and research for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evolutionary optimization of deep learning activation functions")
  Member "bridging nonlinearities and stochastic regularizers with gaussian error linear units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "mixup beyond empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "flexible rectified linear units for improving convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "swish a self gated activation function")
  Member "gaussian error linear units gelus":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")
  Member "parametric exponential linear unit for deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "soft root sign activation function")
  Member "cyclical learning rates for training neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

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)