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: "lisht non parametric linearly scaled hyperbolic tangent activation function for neural networks"
  Abstract: The activation function in neural network is one of the important aspects which facilitates the deep training by introducing the non-linearity into the learning process. However, because of zero-hard rectification, some the of existing activations function such as ReLU and Swish miss to utilize the negative input values and may suffer from the dying gradient problem. Thus, it is important to look for a better activation function which is free from such problems. As a remedy, this paper proposes a new non-parametric function, called Linearly Scaled Hyperbolic Tangent (LiSHT) for Neural Networks (NNs). The proposed LiSHT activation function is an attempt to scale the non-linear Hyperbolic Tangent (Tanh) function by a linear function and tackle the dying gradient problem. The training and classification experiments are performed over benchmark Car Evaluation, Iris, MNIST, CIFAR10, CIFAR100 and twitter140 datasets to show that the proposed activation achieves faster convergence and higher performance. A very promising performance improvement is observed on three different type of neural networks including Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent neural network like Long-short term memory (LSTM). The advantages of proposed activation function are also visualized in terms of the feature activation maps, weight distribution and loss landscape.

Connected through hyperedge HE-1 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network"):
  with members:
    - "3 d deep learning approach for remote sensing image classification"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification"
    - "mask r cnn"
    - "aggregated residual transformations for deep neural networks"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition"):
  with members:
    - "a performance comparison of loss functions for deep face recognition"
    - "hard mining loss based convolutional neural network for face recognition"
    - "average biased relu based cnn descriptor for improved face retrieval"
    - "arcface additive angular margin loss for deep face recognition"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "sphereface deep hypersphere embedding for face recognition"
    - "low resolution face recognition using a two branch deep convolutional neural network architecture"

Connected through hyperedge HE-3 (induced by source paper "hybridsn exploring 3d 2d cnn feature hierarchy for hyperspectral image classification"):
  with members:
    - "3 d deep learning approach for remote sensing image classification"
    - "dual cnn models for unsupervised monocular depth estimation"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "mask r cnn"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

Connected through hyperedge HE-4 (induced by source paper "symmetrical gaussian error linear units sgelus"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "swish a self gated activation function"
    - "gaussian error linear units gelus"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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.CV(Computer Vision and Pattern Recognition)