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: "noisy activation functions"
  Abstract: Common nonlinear activation functions used in neural networks can cause training difficulties due to the saturation behavior of the activation function, which may hide dependencies that are not visible to vanilla-SGD (using first order gradients only). Gating mechanisms that use softly saturating activation functions to emulate the discrete switching of digital logic circuits are good examples of this. We propose to exploit the injection of appropriate noise so that the gradients may flow easily, even if the noiseless application of the activation function would yield zero gradient. Large noise will dominate the noise-free gradient and allow stochastic gradient descent toexplore more. By adding noise only to the problematic parts of the activation function, we allow the optimization procedure to explore the boundary between the degenerate (saturating) and the well-behaved parts of the activation function. We also establish connections to simulated annealing, when the amount of noise is annealed down, making it easier to optimize hard objective functions. We find experimentally that replacing such saturating activation functions by noisy variants helps training in many contexts, yielding state-of-the-art or competitive results on different datasets and task, especially when training seems to be the most difficult, e.g., when curriculum learning is necessary to obtain good results.

Connected through hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "non normal recurrent neural network nnrnn learning long time dependencies while improving expressivity with transient dynamics"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "simple recurrent units for highly parallelizable recurrence"
    - "lstm a search space odyssey"
    - "a flexible approach to automated rnn architecture generation"
    - "residual lstm design of a deep recurrent architecture for distant speech recognition"

Connected through hyperedge HE-2 (induced by source paper "soft root sign activation function"):
  with members:
    - "mish a self regularized non monotonic neural activation function"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improving deep learning by inverse square root linear units isrlus"
    - "invertible residual networks"
    - "the quest for the golden activation function"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "deep learning in memristive nanowire networks"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "attention is all you need"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "wide residual networks"
    - "exploring the limits of language modeling"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "non linear neurons with human like apical dendrite activations"):
  with members:
    - "moroco the moldavian and romanian dialectal corpus"
    - "age progression regression by conditional adversarial autoencoder"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "character level convolutional networks for text classification"
    - "deep residual learning for image recognition"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "towards binary valued gates for robust lstm training"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "energy confused adversarial metric learning for zero shot image retrieval and clustering"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "zero shot sketch image hashing"
    - "almn deep embedding learning with geometrical virtual point generating"
    - "sampling matters in deep embedding learning"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "deep metric learning with angular loss"
    - "no fuss distance metric learning using proxies"
    - "smart mining for deep metric learning"

Connected through hyperedge HE-7 (induced by source paper "l relu piece wise linear activation functions for deep fine grained visual categorization"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "self normalizing neural networks"

Connected through hyperedge HE-8 (induced by source paper "towards non saturating recurrent units for modelling long term dependencies"):
  with members:
    - "can recurrent neural networks warp time"
    - "gated orthogonal recurrent units on learning to forget"
    - "the statistical recurrent unit"
    - "rotational unit of memory"
    - "regularizing and optimizing lstm language models"
    - "memory augmented neural networks with wormhole connections"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "asynchronous methods for deep reinforcement learning"

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)