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 "towards understanding regularization in batch normalization"):
  Abstract: Batch Normalization (BN) improves both convergence and generalization in training neural networks. This work understands these phenomena theoretically. We analyze BN by using a basic block of neural networks, consisting of a kernel layer, a BN layer, and a nonlinear activation function. This basic network helps us understand the impacts of BN in three aspects. First, by viewing BN as an implicit regularizer, BN can be decomposed into population normalization (PN) and gamma decay as an explicit regularization. Second, learning dynamics of BN and the regularization show that training converged with large maximum and effective learning rate. Third, generalization of BN is explored by using statistical mechanics. Experiments demonstrate that BN in convolutional neural networks share the same traits of regularization as the above analyses.

Includes members:
  Member "ssn learning sparse switchable normalization via sparsestmax":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exemplar normalization for learning deep representation")
  Member "switchable whitening for deep representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exemplar normalization for learning deep representation")
  Member "do normalization layers in a deep convnet really need to be distinct":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exemplar normalization for learning deep representation")
  Member "understanding the disharmony between dropout and batch normalization by variance shift":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving")
  Member "differentiable learning to normalize via switchable normalization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adapted center and scale prediction more stable and more accurate")
  Member "globally optimal gradient descent for a convnet with gaussian inputs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "electron proton dynamics in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "span recovery for deep neural networks with applications to input obfuscation")
  Member "l2 regularization versus batch and weight normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking the hyperparameters for fine tuning")

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)