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: "ai benchmark running deep neural networks on android smartphones"
  Abstract: Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.

Connected through hyperedge HE-1 (induced by source paper "replacing mobile camera isp with a single deep learning model"):
  with members:
    - "ai benchmark all about deep learning on smartphones in 2019"
    - "aim 2019 challenge on image demoireing dataset and study"
    - "semantic image synthesis with spatially adaptive normalization"
    - "image super resolution using very deep residual channel attention networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "nice noise injection and clamping estimation for neural network quantization"
    - "fast perceptual image enhancement"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-2 (induced by source paper "learned threshold pruning"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "taxonomy and evaluation of structured compression of convolutional neural networks"
    - "mixconv mixed depthwise convolutional kernels"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-3 (induced by source paper "bit allocation for multi task collaborative intelligence"):
  with members:
    - "multi task learning with compressible features for collaborative intelligence"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "yolov3 an incremental improvement"
    - "deep feature compression for collaborative object detection"
    - "multi task learning as multi objective optimization"
    - "intermediate deep feature compression the next battlefield of intelligent sensing"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-4 (induced by source paper "federated learning for localization a privacy preserving crowdsourcing method"):
  with members:
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"
    - "deep learning in mobile and wireless networking a survey"
    - "a survey of indoor localization systems and technologies"
    - "communication efficient learning of deep networks from decentralized data"
    - "csi based fingerprinting for indoor localization a deep learning approach"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "ai oriented medical workload allocation for hierarchical cloud edge device computing"):
  with members:
    - "edge aibench towards comprehensive end to end edge computing benchmarking"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "pruning convolutional neural networks for resource efficient inference"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "previous a methodology for prediction of visual inference performance on iot devices"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "design automation for efficient deep learning computing"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "effnet an efficient structure for convolutional neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "benchmark analysis of representative deep neural network architectures"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

Connected through hyperedge HE-7 (induced by source paper "seesawfacenets sparse and robust face verification model for mobile platform"):
  with members:
    - "seesaw net convolution neural network with uneven group convolution"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "searching for mobilenetv3"
    - "airface lightweight and efficient model for face recognition"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep recurrent level set for segmenting brain tumors"
    - "cosface large margin cosine loss for deep face recognition"

Connected through hyperedge HE-8 (induced by source paper "ai benchmark all about deep learning on smartphones in 2019"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "on device neural net inference with mobile gpus"
    - "semantic image synthesis with spatially adaptive normalization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "synthetic depth of field with a single camera mobile phone"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "relaxed quantization for discretized neural 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.AI(Artificial Intelligence)