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: "max pooling dropout for regularization of 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 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 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 "improving discrete latent representations with differentiable approximation bridges"):
  with members:
    - "decoupled greedy learning of cnns"
    - "a new benchmark and progress toward improved weakly supervised learning"
    - "deep learning using rectified linear units relu"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "neural arithmetic logic units"
    - "training neural networks using features replay"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"

Connected through hyperedge HE-2 (induced by source paper "a review of meta reinforcement learning for deep neural networks architecture search"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "efficient progressive neural architecture search"
    - "speeding up the hyperparameter optimization of deep convolutional neural networks"
    - "accelerating neural architecture search using performance prediction"
    - "progressive neural architecture search"
    - "efficient architecture search by network transformation"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-3 (induced by source paper "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "instrument orientation based metrics for surgical skill evaluation in robot assisted and open needle driving"
    - "deep learning for time series analysis"
    - "automated surgical skill assessment in rmis training"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "recognizing surgical activities with recurrent neural networks"
    - "multi scale convolutional neural networks for time series classification"
    - "deep learning in neural networks"

Connected through hyperedge HE-4 (induced by source paper "towards principled design of deep convolutional networks introducing simpnet"):
  with members:
    - "understanding neural networks through deep visualization"

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)