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 "the role of invariance in spectral complexity based generalization bounds"):
  Abstract: Deep convolutional neural networks (CNNs) have been shown to be able to fit a random labeling over data while still being able to generalize well for normal labels. Describing CNN capacity through a posteriori measures of complexity has been recently proposed to tackle this apparent paradox. These complexity measures are usually validated by showing that they correlate empirically with GE; being empirically larger for networks trained on random vs normal labels. Focusing on the case of spectral complexity we investigate theoretically and empirically the insensitivity of the complexity measure to invariances relevant to CNNs, and show several limitations of spectral complexity that occur as a result. For a specific formulation of spectral complexity we show that it results in the same upper bound complexity estimates for convolutional and locally connected architectures (which don't have the same favorable invariance properties). This is contrary to common intuition and empirical results.

Includes members:
  Member "size free generalization bounds for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "uniform convergence may be unable to explain generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "information matrices and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scalable and practical natural gradient for large scale deep learning")
  Member "data dependent sample complexity of deep neural networks via lipschitz augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "identifying generalization properties in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dissecting non vacuous generalization bounds based on the mean field approximation")
  Member "stronger generalization bounds for deep nets via a compression approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "understanding generalization and optimization performance of deep cnns":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph neural networks exponentially lose expressive power for node classification")
  Member "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a generalization theory of gradient descent for learning over parameterized deep relu 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)