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 "improving generalization of deep neural networks by leveraging margin distribution"):
  Abstract: Recent researches use margin theory to analyze the generalization performance for deep neural networks. The main results are based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass of information about margin distribution which is crucial to generalization performance. In this paper, we prove a generalization bound dominated by a ratio of the margin standard deviation to the margin mean, where the huge magnitude of spectral norms is reduced. Compared with the spectral norm terms in the existing results, the margin ratio term in our bound is orders of magnitude better in practice. On the other hand, our bound inspires us to optimize the margin ratio. We utilize a convex margin distribution loss function on the deep neural networks to validate our theoretical results. Experiments and visualizations confirm the effectiveness of our approach in terms of performance and representation learning ability.

Includes members:
  Member "stronger generalization bounds for deep nets via a compression approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "a pac bayesian approach to spectrally normalized margin bounds for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "spectrally normalized margin bounds for neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the implicit and explicit regularization effects of dropout")
  Member "learning deep resnet blocks sequentially using boosting theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effects of depth width and initialization a convergence analysis of layer wise training for deep linear neural networks")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "norm based capacity control in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep conditioning treatment of 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)