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: "towards dropout training for convolutional neural networks"
  Abstract: Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.

Connected through hyperedge HE-1 (induced by source paper "probabilistic framework for solving visual dialog"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "attending to discriminative certainty for domain adaptation"
    - "multimodal differential network for visual question generation"
    - "bilinear attention networks"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "learning to count objects in natural images for visual question answering"
    - "differential attention for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "survey of dropout methods for deep neural networks"):
  with members:
    - "effective and efficient dropout for deep convolutional neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "chd consecutive horizontal dropout for human gait feature extraction"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "horizontal pyramid matching for person re identification"
    - "gaitset regarding gait as a set for cross view gait recognition"
    - "in defense of the triplet loss for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "improved dropout for shallow and deep learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "towards better forecasting by fusing near and distant future visions"):
  with members:
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "an overview of multi task learning in deep neural networks"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "on the reduction of variance and overestimation of deep q learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "stochastic variance reduction for deep q learning"
    - "improved regularization of convolutional neural networks with cutout"
    - "learning to play in a day faster deep reinforcement learning by optimality tightening"
    - "unifying count based exploration and intrinsic motivation"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-6 (induced by source paper "repr improved training of convolutional filters"):
  with members:
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"
    - "focal loss for dense object detection"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "thinet a filter level pruning method for deep neural network compression"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "channel pruning for accelerating very deep neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"

Connected through hyperedge HE-7 (induced by source paper "excitation dropout encouraging plasticity in deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "shakeout a new approach to regularized deep neural network training"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "improved dropout for shallow and deep learning"
    - "top down neural attention by excitation backprop"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "data dropout in arbitrary basis for deep network regularization"):
  with members:
    - "improved dropout for shallow and deep learning"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "a neural conversational model"
    - "adam a method for stochastic optimization"
    - "analyzing noise in autoencoders and deep networks"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

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)