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: "a quantitative analysis of the effect of batch normalization on gradient descent"
  Abstract: Despite its empirical success and recent theoretical progress, there generally lacks a quantitative analysis of the effect of batch normalization (BN) on the convergence and stability of gradient descent. In this paper, we provide such an analysis on the simple problem of ordinary least squares (OLS). Since precise dynamical properties of gradient descent (GD) is completely known for the OLS problem, it allows us to isolate and compare the additional effects of BN. More precisely, we show that unlike GD, gradient descent with BN (BNGD) converges for arbitrary learning rates for the weights, and the convergence remains linear under mild conditions. Moreover, we quantify two different sources of acceleration of BNGD over GD -- one due to over-parameterization which improves the effective condition number and another due having a large range of learning rates giving rise to fast descent. These phenomena set BNGD apart from GD and could account for much of its robustness properties. These findings are confirmed quantitatively by numerical experiments, which further show that many of the uncovered properties of BNGD in OLS are also observed qualitatively in more complex supervised learning problems.

Connected through hyperedge HE-1 (induced by source paper "instance enhancement batch normalization an adaptive regulator of batch noise"):
  with members:
    - "instance level meta normalization"
    - "spatial group wise enhance improving semantic feature learning in convolutional networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "selective kernel networks"
    - "dianet dense and implicit attention network"
    - "group normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "towards understanding regularization in batch normalization"

Connected through hyperedge HE-2 (induced by source paper "implicit regularization of normalization methods"):
  with members:
    - "over parameterization as a catalyst for better generalization of deep relu network"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "implicit regularization in deep matrix factorization"
    - "theoretical issues in deep networks approximation optimization and generalization"
    - "luck matters understanding training dynamics of deep relu networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theoretical analysis of auto rate tuning by batch normalization"

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)