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: "improving deep learning by inverse square root linear units isrlus"
  Abstract: We introduce the “inverse square root linear unit” (ISRLU) to speed up learning in deep neural networks. ISRLU has better performance than ELU but has many of the same benefits. ISRLU and ELU have similar curves and characteristics. Both have negative values, allowing them to push mean unit activation closer to zero, and bring the normal gradient closer to the unit natural gradient, ensuring a noise- robust deactivation state, lessening the over fitting risk. The significant performance advantage of ISRLU on traditional CPUs also carry over to more efficient HW implementations on HW/SW codesign for CNNs/RNNs. In experiments with TensorFlow, ISRLU leads to faster learning and better generalization than ReLU on CNNs. This work also suggests a computationally efficient variant called the “inverse square root unit” (ISRU) which can be used for RNNs. Many RNNs use either long short-term memory (LSTM) and gated recurrent units (GRU) which are implemented with tanh and sigmoid activation functions. ISRU has less computational complexity but still has a similar curve to tanh and sigmoid.

Connected through hyperedge HE-1 (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"
    - "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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-2 (induced by source paper "goldilocks neural networks"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "a continuum among logarithmic linear and exponential functions and its potential to improve generalization in neural networks"
    - "deep learning with s shaped rectified linear activation units"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "empirical evaluation of rectified activations in convolutional network"

Connected through hyperedge HE-3 (induced by source paper "learn able parameter guided activation functions"):
  with members:
    - "activation functions comparison of trends in practice and research for deep learning"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "mixup beyond empirical risk minimization"
    - "flexible rectified linear units for improving convolutional neural networks"
    - "gaussian error linear units gelus"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "cyclical learning rates for training neural networks"
    - "no more pesky learning rate guessing games"

Connected through hyperedge HE-4 (induced by source paper "hebbian descent"):
  with members:
    - "self normalizing neural networks"
    - "one shot learning with memory augmented neural networks"
    - "overcoming catastrophic forgetting in neural networks"
    - "an overview of gradient descent optimization algorithms"
    - "conservativeness of untied auto encoders"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "flexible rectified linear units for improving convolutional neural networks"):
  with members:
    - "self normalizing neural networks"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "deep residual networks with exponential linear unit"
    - "revise saturated activation functions"
    - "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"

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)