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: "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
  Abstract: It is desirable to train convolutional networks (CNNs) to run more efficiently during inference. In many cases however, the computational budget that the system has for inference cannot be known beforehand during training, or the inference budget is dependent on the changing real-time resource availability. Thus, it is inadequate to train just inference-efficient CNNs, whose inference costs are not adjustable and cannot adapt to varied inference budgets. We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint). During training, SDPoint applies feature map downsampling to a random point in the layer hierarchy, with a random downsampling ratio. The different stochastic downsampling configurations known as SDPoint instances (of the same model) have computational costs different from each other, while being trained to minimize the same prediction loss. Sharing network parameters across different instances provides significant regularization boost. During inference, one may handpick a SDPoint instance that best fits the inference budget. The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizer, is validated through extensive experiments on image classification.

Connected through hyperedge HE-1 (induced by source paper "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"):
  with members:
    - "searching for mobilenetv3"
    - "single path one shot neural architecture search with uniform sampling"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "superposition of many models into one"
    - "energy and policy considerations for deep learning in nlp"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (induced by source paper "be your own teacher improve the performance of convolutional neural networks via self distillation"):
  with members:
    - "slimmable neural networks"
    - "ktan knowledge transfer adversarial network"
    - "meal multi model ensemble via adversarial learning"
    - "label refinery improving imagenet classification through label progression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "focal loss for dense object detection"
    - "deep mutual learning"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-3 (induced by source paper "recurrent convolution for compact and cost adjustable neural networks an empirical study"):
  with members:
    - "slimmable neural networks"
    - "doubly nested network for resource efficient inference"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "multi scale dense networks for resource efficient image classification"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "towards the limit of network quantization"

Connected through hyperedge HE-4 (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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "big little net an efficient multi scale feature representation for visual and speech recognition"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "cbam convolutional block attention module"
    - "skipnet learning dynamic routing in convolutional networks"
    - "progressive neural architecture search"
    - "english conversational telephone speech recognition by humans and machines"
    - "learning transferable architectures for scalable image recognition"
    - "snapshot ensembles train 1 get m for free"

Connected through hyperedge HE-6 (induced by source paper "slimmable neural networks"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "ai benchmark running deep neural networks on android smartphones"
    - "efficient video object segmentation via network modulation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "demystifying neural style transfer"

Connected through hyperedge HE-7 (induced by source paper "instanas instance aware neural architecture search"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "dpp net device aware progressive search for pareto optimal neural architectures"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "darts differentiable architecture search"

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)