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 "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  Abstract: Regularization and normalization have become indispensable components in training deep neural networks, resulting in faster training and improved generalization performance. We propose the projected error function regularization loss (PER) that encourages activations to follow the standard normal distribution. PER randomly projects activations onto one-dimensional space and computes the regularization loss in the projected space. PER is similar to the Pseudo-Huber loss in the projected space, thus taking advantage of both $L^1$ and $L^2$ regularization losses. Besides, PER can capture the interaction between hidden units by projection vector drawn from a unit sphere. By doing so, PER minimizes the upper bound of the Wasserstein distance of order one between an empirical distribution of activations and the standard normal distribution. To the best of the authors' knowledge, this is the first work to regularize activations via distribution matching in the probability distribution space. We evaluate the proposed method on the image classification task and the word-level language modeling task.

Includes members:
  Member "fixup initialization residual learning without normalization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rethinking batch normalization in transformers")
  Member "improving sequence to sequence learning via optimal transport":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distilling the knowledge of bert for text generation")
  Member "iterative normalization beyond standardization towards efficient whitening":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concept whitening for interpretable image recognition")
  Member "dropblock a regularization method for convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "policy optimization as wasserstein gradient flows":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving sequence to sequence learning via optimal transport")
  Member "understanding batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on feature normalization and data augmentation")
  Member "decorrelated batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an internal covariate shift bounding algorithm for deep neural networks by unitizing layers outputs")
  Member "measuring and regularizing networks in function space":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the distance between two neural networks and the stability of 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)