human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "effective and efficient dropout for deep convolutional neural networks"
  Abstract: Machine-learning-based data-driven applications have become ubiquitous, e.g., health-care analysis and database system optimization. Big training data and large (deep) models are crucial for good performance. Dropout has been widely used as an efficient regularization technique to prevent large models from overfitting. However, many recent works show that dropout does not bring much performance improvement for deep convolutional neural networks (CNNs), a popular deep learning model for data-driven applications. In this paper, we revisit the problem and investigate its failure. We attribute the failure to the conflict between the conventional dropout and the batch normalization operation after it. We propose to adjust the order of the dropout operations to address the conflict; and further, other structurally more suited dropout variants are also examined and introduced for more efficient and effective regularization for CNNs. These dropout variants can be easily integrated into the building blocks of CNNs implemented by existing deep learning libraries, e.g., Apache Singa, to provide effective regularization for CNNs. Extensive experiments on benchmark datasets CIFAR, SVHN and ImageNet are conducted to compare the existing building blocks and the proposed building blocks with the proposed customizable dropout methods. The results confirm the superiority of our building blocks due to the regularization and implicit model ensemble effect of dropout. In particular, we improve over state-of-the-art CNNs with significantly better performance of 3.17%, 16.15%, 1.44%, 21.68% error rate on CIFAR-10, CIFAR-100, SVHN and ImageNet respectively.

Connected through hyperedge HE-1 (induced by source paper "survey of dropout methods for deep neural networks"):
  with members:
    - "adversarial dropout for recurrent neural networks"
    - "ising dropout a regularization method for training and compression of deep neural networks"
    - "recent advances in recurrent neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "regularizing and optimizing lstm language models"
    - "improved regularization of convolutional neural networks with cutout"
    - "adversarial dropout regularization"

Connected through hyperedge HE-2 (induced by source paper "understanding dropout as an optimization trick"):
  with members:
    - "multi sample dropout for accelerated training and better generalization"
    - "learning rate dropout"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "yolo9000 better faster stronger"
    - "noisy activation functions"

Question: Please tell me which class the center node 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)