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 "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds"):
  Abstract: A recent line of work in deep learning theory has utilized the mean-field analysis to demonstrate the global convergence of noisy (stochastic) gradient descent for training over-parameterized two-layer neural networks. However, existing results in the mean-field setting do not provide the convergence rate of neural network training, and the generalization error bound is largely missing. In this paper, we provide a mean-field analysis in a generalized neural tangent kernel regime, and show that noisy gradient descent with weight decay can still exhibit a "kernel-like" behavior. This implies that the training loss converges linearly up to a certain accuracy in such regime. We also establish a generalization error bound for two-layer neural networks trained by noisy gradient descent with weight decay. Our results shed light on the connection between mean field analysis and the neural tangent kernel based analysis.

Includes members:
  Member "backward feature correction how deep learning performs deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "how much over parameterization is sufficient to learn deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "beyond linearization on quadratic and higher order approximation of wide neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "taylorized training towards better approximation of neural network training at finite width")
  Member "towards understanding the spectral bias of deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "frequency bias in neural networks for input of non uniform density")
  Member "an improved analysis of training over parameterized deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "generalization error bounds of gradient descent for learning over parameterized deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "towards moderate overparameterization global convergence guarantees for training shallow neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "over parameterized two level neural networks can learn near optimal feature representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "how much over parameterization is sufficient to learn deep relu networks")

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)