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: "prunetrain gradual structured pruning from scratch for faster neural network training"
  Abstract: Model pruning is a popular mechanism to make a network more efficient for inference. In this paper, we explore the use of pruning to also make the training of such neural networks more efficient. Unlike all prior model pruning methods that sparsify a pre-trained model and then prune it, we train the network from scratch, while gradually and structurally pruning parameters during the training. We build on our key observations: 1) once parameters are sparsified via regularization, they rarely re-appear in later steps, and 2) setting the appropriate regularization penalty at the beginning of training effectively converges the loss. We train ResNet and VGG networks on CIFAR10/100 and ImageNet datasets from scratch, and achieve 30-50% improvement in training FLOPs and 20-30% improvement in measured training time on modern GPUs.

Connected through hyperedge HE-1 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators"):
  with members:
    - "low memory neural network training a technical report"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "network compression using correlation analysis of layer responses"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "accelerated cnn training through gradient approximation"):
  with members:
    - "training simplification and model simplification for deep learning a minimal effort back propagation method"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "biologically plausible learning algorithms can scale to large datasets"
    - "nvidia tensor core programmability performance precision"
    - "faster neural network training with approximate tensor operations"
    - "sbnet sparse blocks network for fast inference"
    - "dropback continuous pruning during training"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"

Connected through hyperedge HE-3 (induced by source paper "model pruning enables efficient federated learning on edge devices"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "performance analysis and characterization of training deep learning models on nvidia tx2"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"

Connected through hyperedge HE-4 (induced by source paper "stabilizing the lottery ticket hypothesis"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "the state of sparsity in deep neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "snip single shot network pruning based on connection sensitivity"
    - "rethinking the value of network pruning"
    - "deep rewiring training very sparse deep networks"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-5 (induced by source paper "training cnns faster with dynamic input and kernel downsampling"):
  with members:
    - "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"
    - "fast training of convolutional neural networks via kernel rescaling"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"

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.LG(Machine Learning)