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: "parametric exponential linear unit for deep convolutional neural networks"
  Abstract: Object recognition is an important task for improving the ability of visual systems to perform complex scene understanding. Recently, the Exponential Linear Unit (ELU) has been proposed as a key component for managing bias shift in Convolutional Neural Networks (CNNs), but defines a parameter that must be set by hand. In this paper, we propose learning a parameterization of ELU in order to learn the proper activation shape at each layer in the CNNs. Our results on the MNIST, CIFAR-10/100 and ImageNet datasets using the NiN, Overfeat, All-CNN and ResNet networks indicate that our proposed Parametric ELU (PELU) has better performances than the non-parametric ELU. We have observed as much as a 7.28% relative error improvement on ImageNet with the NiN network, with only 0.0003% parameter increase. Our visual examination of the non-linear behaviors adopted by Vgg using PELU shows that the network took advantage of the added flexibility by learning different activations at different layers.

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"
    - "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-2 (induced by source paper "cooperative initialization based deep neural network training"):
  with members:
    - "accuracy booster performance boosting using feature map re calibration"
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "leveraging filter correlations for deep model compression"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-4 (induced by source paper "d3s a discriminative single shot segmentation tracker"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "atom accurate tracking by overlap maximization"
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"

Connected through hyperedge HE-5 (induced by source paper "motion planning networks bridging the gap between learning based and classical motion planners"):
  with members:
    - "neural path planning fixed time near optimal path generation via oracle imitation"
    - "composing task agnostic policies with deep reinforcement learning"
    - "composing ensembles of policies with deep reinforcement learning"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "experience replay for continual learning"

Connected through hyperedge HE-6 (induced by source paper "insights into lstm fully convolutional networks for time series classification"):
  with members:
    - "deep gated recurrent and convolutional network hybrid model for univariate time series classification"
    - "multivariate lstm fcns for time series classification"
    - "lstm fully convolutional networks for time series classification"
    - "the ucr time series archive"
    - "fast and accurate time series classification with weasel"
    - "asymmetric learning vector quantization for efficient nearest neighbor classification in dynamic time warping spaces"
    - "squeeze and excitation networks"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-7 (induced by source paper "a simple and efficient architecture for trainable activation functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "self normalizing neural networks"
    - "neural network with unbounded activation functions is universal approximator"
    - "searching for activation functions"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a single hidden layer feedforward network with only one neuron in the hidden layer can approximate any univariate function"
    - "the power of depth for feedforward neural networks"
    - "deep learning with s shaped rectified linear activation units"

Connected through hyperedge HE-8 (induced by source paper "probact a probabilistic activation function for deep neural networks"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "the quest for the golden activation function"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "towards robust neural networks via random self ensemble"
    - "noisy activation functions"
    - "deep learning in neural networks"
    - "deep learning with s shaped rectified linear activation units"

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)