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 "can sgd learn recurrent neural networks with provable generalization"):
  Abstract: Recurrent Neural Networks (RNNs) are among the most popular models in sequential data analysis. However, due to the complexity raised by recurrent structure, they remain one of the least theoretically understood neural-network models. In particular, existing generalization bounds for RNNs mostly scale exponentially with the length of the input sequence, which limited their practical implications. In this paper, we show that if the input labels are (approximately) realizable by certain classes of (non-linear) functions of the input sequences, then using the vanilla stochastic gradient descent (SGD), RNNs can actually learn them $\textit{efficiently}$, meaning that both the training time and sample complexity only scale $\textit{polynomially}$ with the input length (or almost polynomially, depending on the classes of non-linearity).

Includes members:
  Member "learning and generalization in overparameterized neural networks going beyond two layers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "on the convergence rate of training recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "stabilizing gradients for deep neural networks via efficient svd parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "theoretical properties of the global optimizer of two layer neural network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "recent advances in recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")
  Member "a convergence theory for deep learning via over parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "on generalization bounds of a family of recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generalization and representational limits of 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)