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 "convolutional neural networks with dynamic regularization"):
  Abstract: Regularization is commonly used in machine learning for alleviating overfitting. In convolutional neural networks, regularization methods, such as Dropout and Shake-Shake, have been proposed to improve the generalization performance. However, these methods are lack of self-adaption throughout training, i.e., the regularization strength is fixed to a predefined schedule, and manual adjustment has to be performed to adapt to various network architectures. In this paper, we propose a dynamic regularization method which can dynamically adjust the regularization strength in the training procedure. Specifically, we model the regularization strength as a backward difference of the training loss, which can be directly extracted in each training iteration. With dynamic regularization, the large model is regularized by the strong perturbation and vice versa. Experimental results show that the proposed method can improve the generalization capability of off-the-shelf network architectures and outperforms state-of-the-art regularization methods.

Includes members:
  Member "continuous dropout":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building computationally efficient and well generalizing person re identification models with metric learning")
  Member "shakedrop regularization for deep residual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "object detection with deep learning a review":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "learning transferable architectures for scalable image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "improved regularization of convolutional neural networks with cutout":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on feature normalization and data augmentation")
  Member "shake shake regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimized generic feature learning for few shot classification across domains")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "deep networks with stochastic depth":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

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.CV(Computer Vision and Pattern Recognition)