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 "on the infinite width limit of neural networks with a standard parameterization"):
  Abstract: There are currently two parameterizations used to derive fixed kernels corresponding to infinite width neural networks, the NTK (Neural Tangent Kernel) parameterization and the naive standard parameterization. However, the extrapolation of both of these parameterizations to infinite width is problematic. The standard parameterization leads to a divergent neural tangent kernel while the NTK parameterization fails to capture crucial aspects of finite width networks such as: the dependence of training dynamics on relative layer widths, the relative training dynamics of weights and biases, and a nonstandard learning rate scale. Here we propose an improved extrapolation of the standard parameterization that preserves all of these properties as width is taken to infinity and yields a well-defined neural tangent kernel. We show experimentally that the resulting kernels typically achieve similar accuracy to those resulting from an NTK parameterization, but with better correspondence to the parameterization of typical finite width networks. Additionally, with careful tuning of width parameters, the improved standard parameterization kernels can outperform those stemming from an NTK parameterization. We release code implementing this improved standard parameterization as part of the Neural Tangents library at this https URL.

Includes members:
  Member "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "batch normalization biases deep residual networks towards shallow paths")
  Member "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "asymptotics of wide networks from feynman diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study")
  Member "harnessing the power of infinitely wide deep nets on small data tasks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "taylorized training towards better approximation of neural network training at finite width")
  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 "batch normalization biases deep residual networks towards shallow paths")
  Member "on exact computation with an infinitely wide neural net":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "information in infinite ensembles of infinitely wide neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "forgetting outside the box scrubbing deep networks of information accessible from input output observations")
  Member "graph neural tangent kernel fusing graph neural networks with graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "evaluating logical generalization in graph neural 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)