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: "revise saturated activation functions"
  Abstract: In this paper, we revise two commonly used saturated functions, the logistic sigmoid and the hyperbolic tangent (tanh). #R##N#We point out that, besides the well-known non-zero centered property, slope of the activation function near the origin is another possible reason making training deep networks with the logistic function difficult to train. We demonstrate that, with proper rescaling, the logistic sigmoid achieves comparable results with tanh. #R##N#Then following the same argument, we improve tahn by penalizing in the negative part. We show that "penalized tanh" is comparable and even outperforms the state-of-the-art non-saturated functions including ReLU and leaky ReLU on deep convolution neural networks. #R##N#Our results contradict to the conclusion of previous works that the saturation property causes the slow convergence. It suggests further investigation is necessary to better understand activation functions in deep architectures.

Connected through hyperedge HE-1 (induced by source paper "effect of activation functions on the training of overparametrized neural nets"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-2 (induced by source paper "is it time to swish comparing deep learning activation functions across nlp tasks"):
  with members:
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "self normalizing neural networks"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "searching for activation functions"
    - "cross lingual and cross domain discourse segmentation of entire documents"
    - "parsing argumentation structures in persuasive essays"

Connected through hyperedge HE-3 (induced by source paper "random bias initialization improving binary neural network training"):
  with members:
    - "activation adaptation in neural networks"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "noisy activation functions"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "auto rotating perceptrons"):
  with members:
    - "activation functions comparison of trends in practice and research for deep learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a new backpropagation algorithm without gradient descent"):
  with members:
    - "swish a self gated activation function"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-6 (induced by source paper "plu the piecewise linear unit activation function"):
  with members:
    - "searching for activation functions"

Connected through hyperedge HE-7 (induced by source paper "flexible rectified linear units for improving convolutional neural networks"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "self normalizing neural networks"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "deep residual networks with exponential linear unit"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "improving deep neural network with multiple parametric exponential linear units"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "all you need is a good init"

Connected through hyperedge HE-8 (induced by source paper "empirical analysis of non linear activation functions for deep neural networks in classification tasks"):
  with members:
    - "self normalizing neural networks"
    - "fast and accurate deep network learning by exponential linear units elus"

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)