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 "deep learning for inverse problems bounds and regularizers"):
  Abstract: Inverse problems arise in a number of domains such as medical imaging, remote sensing, and many more, relying on the use of advanced signal and image processing approaches -- such as sparsity-driven techniques -- to determine their solution. This paper instead studies the use of deep learning approaches to approximate the solution of inverse problems. In particular, the paper provides a new generalization bound, depending on key quantity associated with a deep neural network -- its Jacobian matrix -- that also leads to a number of computationally efficient regularization strategies applicable to inverse problems. The paper also tests the proposed regularization strategies in a number of inverse problems including image super-resolution ones. Our numerical results conducted on various datasets show that both fully connected and convolutional neural networks regularized using the regularization or proxy regularization strategies originating from our theory exhibit much better performance than deep networks regularized with standard approaches such as weight-decay.

Includes members:
  Member "the singular values of convolutional layers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "towards understanding the role of over parametrization in generalization of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "lipschitz margin training scalable certification of perturbation invariance for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "generalization error in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding generalization in deep learning via tensor methods")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "can we gain more from orthogonality regularizations in training deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self orthogonality module a network architecture plug in for learning orthogonal filters")
  Member "generalizable adversarial training via spectral normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gans may have no nash equilibria")
  Member "stronger generalization bounds for deep nets via a compression approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")

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)