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: "sleep quality prediction from wearable data using deep learning"
  Abstract: Background: The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science. Objective: The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality. Methods: Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM). Results: Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional logistic regression. “CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional logistic regression (0.6463). Conclusions: Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.  [JMIR Mhealth Uhealth 2016;4(4):e125]

Connected through hyperedge HE-1 (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"
    - "ai benchmark running deep neural networks on android smartphones"

Connected through hyperedge HE-2 (induced by source paper "ai benchmark running deep neural networks on android smartphones"):
  with members:
    - "a quantization friendly separable convolution for mobilenets"
    - "pirm challenge on perceptual image enhancement on smartphones report"
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "icnet for real time semantic segmentation on high resolution images"
    - "a deep reinforcement learning chatbot"

Connected through hyperedge HE-3 (induced by source paper "adversarial unsupervised representation learning for activity time series"):
  with members:
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "node2vec scalable feature learning for networks"
    - "deep activity recognition models with triaxial accelerometers"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "representation learning a review and new perspectives"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "co morbidity exploration on wearables activity data using unsupervised pre training and multi task learning"):
  with members:
    - "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "multi task prediction of disease onsets from longitudinal lab tests"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "node2vec scalable feature learning for networks"
    - "conditional random fields as recurrent neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)