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: "fast training of convolutional neural networks via kernel rescaling"
  Abstract: Training deep Convolutional Neural Networks (CNN) is a time consuming task that may take weeks to complete. In this article we propose a novel, theoretically founded method for reducing CNN training time without incurring any loss in accuracy. The basic idea is to begin training with a pre-train network using lower-resolution kernels and input images, and then refine the results at the full resolution by exploiting the spatial scaling property of convolutions. We apply our method to the ImageNet winner OverFeat and to the more recent ResNet architecture and show a reduction in training time of nearly 20% while test set accuracy is preserved in both cases.

Connected through hyperedge HE-1 (induced by source paper "training cnns faster with dynamic input and kernel downsampling"):
  with members:
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "super convergence very fast training of residual networks using large learning rates"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "accelerating training using tensor decomposition"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "lightweight monocular depth estimation model by joint end to end filter pruning"
    - "importance estimation for neural network pruning"
    - "energy and policy considerations for deep learning in nlp"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "network decoupling from regular to depthwise separable convolutions"
    - "rethinking the value of network pruning"

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)