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: "the quest for the golden activation function"
  Abstract: Deep Neural Networks have been shown to be beneficial for a variety of tasks, in particular allowing for end-to-end learning and reducing the requirement for manual design decisions. However, still many parameters have to be chosen in advance, also raising the need to optimize them. One important, but often ignored system parameter is the selection of a proper activation function. Thus, in this paper we target to demonstrate the importance of activation functions in general and show that for different tasks different activation functions might be meaningful. To avoid the manual design or selection of activation functions, we build on the idea of genetic algorithms to learn the best activation function for a given task. In addition, we introduce two new activation functions, ELiSH and HardELiSH, which can easily be incorporated in our framework. In this way, we demonstrate for three different image classification benchmarks that different activation functions are learned, also showing improved results compared to typically used baselines.

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"
    - "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 "neural network architecture search with differentiable cartesian genetic programming for regression"):
  with members:
    - "eat nas elastic architecture transfer for accelerating large scale neural architecture search"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "efficient architecture search by network transformation"
    - "searching for activation functions"
    - "pmlb a large benchmark suite for machine learning evaluation and comparison"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-3 (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"
    - "searching for activation functions"
    - "swish a self gated activation function"
    - "towards robust neural networks via random self ensemble"
    - "noisy activation functions"
    - "parametric exponential linear unit for deep convolutional neural networks"
    - "deep learning in neural networks"
    - "deep learning with s shaped rectified linear activation units"

Connected through hyperedge HE-4 (induced by source paper "activation functions comparison of trends in practice and research for deep learning"):
  with members:
    - "self normalizing neural networks"
    - "safe mutations for deep and recurrent neural networks through output gradients"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning to segment every thing"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "fractalnet ultra deep neural networks without residuals"
    - "parametric exponential linear unit for deep convolutional 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.NE(Neural and Evolutionary Computing)