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 distance between two neural networks and the stability of learning"):
  Abstract: How far apart are two neural networks? This is a foundational question in their theory. We derive a simple and tractable bound that relates distance in function space to distance in parameter space for a broad class of nonlinear compositional functions. The bound distills a clear dependence on depth of the composition. The theory is of practical relevance since it establishes a trust region for first-order optimisation. In turn, this suggests an optimiser that we call Frobenius matched gradient descent---or Fromage. Fromage involves a principled form of gradient rescaling and enjoys guarantees on stability of both the spectra and Frobenius norms of the weights. We find that the new algorithm increases the depth at which a multilayer perceptron may be trained as compared to Adam and SGD and is competitive with Adam for training generative adversarial networks. We further verify that Fromage scales up to a language transformer with over $10^8$ parameters. Please find code & reproducibility instructions at: https://github.com/jxbz/fromage.

Includes members:
  Member "optimization for deep learning theory and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "laprop a better way to combine momentum with adaptive gradient")
  Member "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "think global act local relating dnn generalisation and node level snr")
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unsupervised question decomposition for question answering")
  Member "stochastic gradient mirror descent minimax optimality and implicit regularization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting can be harmless for basis pursuit only to a degree")
  Member "sorting out lipschitz function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial robustness through local lipschitzness")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "large scale gan training for high fidelity natural image synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "measuring and regularizing networks in function space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric")

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)