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: "activation functions comparison of trends in practice and research for deep learning"
  Abstract: Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.

Connected through hyperedge HE-1 (induced by source paper "evolutionary optimization of deep learning activation functions"):
  with members:
    - "a survey on neural architecture search"
    - "progressive differentiable architecture search bridging the depth gap between search and evaluation"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "regularized evolution for image classifier architecture search"
    - "large scale evolution of image classifiers"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "swish a self gated activation function"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "neural network representation control gaussian isolation machines and cvc regularization"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "anomaly detection using one class neural networks"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "robust deep and inductive anomaly detection"
    - "generic black box end to end attack against state of the art api call based malware classifiers"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (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-4 (induced by source paper "generating accurate pseudo labels via hermite polynomials for ssl confidently"):
  with members:
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "label propagation for deep semi supervised learning"
    - "joint optimization framework for learning with noisy labels"
    - "saas speed as a supervisor for semi supervised learning"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "learning one hidden layer neural networks with landscape design"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-5 (induced by source paper "improved cardinality estimation by learning queries containment rates"):
  with members:
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"
    - "deep reinforcement learning for join order enumeration"
    - "learning to optimize join queries with deep reinforcement learning"
    - "the case for learned index structures"
    - "overcoming catastrophic forgetting in neural networks"
    - "a fast unified model for parsing and sentence understanding"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-6 (induced by source paper "k tanh hardware efficient activations for deep learning"):
  with members:
    - "a study of bfloat16 for deep learning training"
    - "energy and policy considerations for deep learning in nlp"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "searching for activation functions"
    - "gaussian error linear units gelus"

Connected through hyperedge HE-7 (induced by source paper "learn able parameter guided activation functions"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "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-8 (induced by source paper "device free user authentication activity classification and tracking using passive wi fi sensing a deep learning based approach"):
  with members:
    - "a scalable deep neural network architecture for multi building and multi floor indoor localization based on wi fi fingerprinting"
    - "densely connected convolutional networks"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "adam a method for stochastic optimization"
    - "two stream convolutional networks for action recognition in videos"
    - "speech recognition with deep recurrent neural networks"

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)