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: "flexible rectified linear units for improving convolutional neural networks"
  Abstract: Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks. In this paper, we propose a novel activation function called flexible rectified linear unit (FReLU). FReLU improves the flexibility of ReLU by a learnable rectified point. FReLU achieves a faster convergence and higher performance. Furthermore, FReLU does not rely on strict assumptions by self-adaption. FReLU is also simple and effective without using exponential function. We evaluate FReLU on two standard image classification dataset, including CIFAR-10 and CIFAR-100. Experimental results show the strengths of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learn able parameter guided activation functions"):
  with members:
    - "improving deep learning by inverse square root linear units isrlus"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "mixup beyond empirical risk minimization"
    - "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-2 (induced by source paper "swish a self gated activation function"):
  with members:
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "self normalizing neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "efficient architecture search by network transformation"
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"

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)