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: "weighted sigmoid gate unit for an activation function of deep neural network"
  Abstract: An activation function has crucial role in a deep neural network. #R##N#A simple rectified linear unit (ReLU) are widely used for the activation function. #R##N#In this paper, a weighted sigmoid gate unit (WiG) is proposed as the activation function. #R##N#The proposed WiG consists of a multiplication of inputs and the weighted sigmoid gate. #R##N#It is shown that the WiG includes the ReLU and same activation functions as a special case. #R##N#Many activation functions have been proposed to overcome the performance of the ReLU. #R##N#In the literature, the performance is mainly evaluated with an object recognition task. #R##N#The proposed WiG is evaluated with the object recognition task and the image restoration task. #R##N#Then, the expeirmental comparisons demonstrate the proposed WiG overcomes the existing activation functions including the ReLU.

Connected through hyperedge HE-1 (induced by source paper "interpretation of feature space using multi channel attentional sub networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "improving transparency of deep neural inference process"
    - "visual interpretability for deep learning a survey"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "multi scale context aggregation by dilated convolutions"
    - "deep learning face attributes in the wild"

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)