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 "learning compact neural networks with regularization"):
  Abstract: We study the impact of regularization for learning neural networks. Our goal is speeding up training, improving generalization performance, and training compact models that are cost efficient. Our results apply to weight-sharing (e.g.~convolutional), sparsity (i.e.~pruning), and low-rank constraints among others. We first introduce covering dimension of the constraint set and provide a Rademacher complexity bound providing insights on generalization properties. Then, we propose and analyze regularized gradient descent algorithms for learning shallow networks. We show that problem becomes well conditioned and local linear convergence occurs once the amount of data exceeds covering dimension (e.g.~\# of nonzero weights). Finally, we provide insights on layerwise training of deep models by studying a random activation model. Our results show how regularization can be beneficial to overcome overparametrization.

Includes members:
  Member "end to end learning of a convolutional neural network via deep tensor decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning two layer neural networks with symmetric inputs")
  Member "learning one hidden layer neural networks with landscape design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "beating the perils of non convexity guaranteed training of neural networks using tensor methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")
  Member "learning relus via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mean field analysis of two layer neural networks non asymptotic rates and generalization bounds")
  Member "globally optimal gradient descent for a convnet with gaussian inputs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "recovery guarantees for one hidden layer neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "theoretical insights into the optimization landscape of over parameterized shallow neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs deep learning")

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)