human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "average biased relu based cnn descriptor for improved face retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "psnet parametric sigmoid norm based cnn for face recognition")
  Member "complex valued neural networks with non parametric activation functions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multikernel activation functions formulation and a case study")
  Member "dual cnn models for unsupervised monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning hyperspectral feature extraction and classification with resnext network")
  Member "a performance evaluation of convolutional neural networks for face anti spoofing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep spatial gradient and temporal depth learning for face anti spoofing")
  Member "rccnet an efficient convolutional neural network for histological routine colon cancer nuclei classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "impact of fully connected layers on performance of convolutional neural networks for image classification")
  Member "bridging nonlinearities and stochastic regularizers with gaussian error linear units":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "xgpt cross modal generative pre training for image captioning")
  Member "dfunet convolutional neural networks for diabetic foot ulcer classification":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "semantic segmentation of human thigh quadriceps muscle in magnetic resonance images")
  Member "visualizing the loss landscape of neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")

Question: Please tell me which class the source paper 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)