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 "batch normalization biases deep residual networks towards shallow paths"):
  Abstract: Batch normalization has multiple benefits. It improves the conditioning of the loss landscape, and is a surprisingly effective regularizer. However, the most important benefit of batch normalization arises in residual networks, where it dramatically increases the largest trainable depth. We identify the origin of this benefit: At initialization, batch normalization downscales the residual branch relative to the skip connection, by a normalizing factor proportional to the square root of the network depth. This ensures that, early in training, the function computed by deep normalized residual networks is dominated by shallow paths with well-behaved gradients. We use this insight to develop a simple initialization scheme which can train very deep residual networks without normalization. We also clarify that, although batch normalization does enable stable training with larger learning rates, this benefit is only useful when one wishes to parallelize training over large batch sizes. Our results help isolate the distinct benefits of batch normalization in different architectures.

Includes members:
  Member "provable benefit of orthogonal initialization in optimizing deep linear networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the global convergence of training deep linear resnets")
  Member "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization")
  Member "filter response normalization layer eliminating batch dependence in the training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "be like water robustness to extraneous variables via adaptive feature normalization")
  Member "a mean field theory of batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "the effect of network width on stochastic gradient descent and generalization an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "global convergence of deep networks with one wide layer followed by pyramidal topology")
  Member "fixup initialization residual learning without normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "efficientnet rethinking model scaling for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization")

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)