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: "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system"
  Abstract: Background: Regular physical activity is known to be beneficial for people with type 2 diabetes. Nevertheless, most of the people who have diabetes lead a sedentary lifestyle. Smartphones create new possibilities for helping people to adhere to their physical activity goals through continuous monitoring and communication, coupled with personalized feedback. Objective: The aim of this study was to help type 2 diabetes patients increase the level of their physical activity. Methods: We provided 27 sedentary type 2 diabetes patients with a smartphone-based pedometer and a personal plan for physical activity. Patients were sent short message service messages to encourage physical activity between once a day and once per week. Messages were personalized through a Reinforcement Learning algorithm so as to improve each participant’s compliance with the activity regimen. The algorithm was compared with a static policy for sending messages and weekly reminders. Results: Our results show that participants who received messages generated by the learning algorithm increased the amount of activity and pace of walking, whereas the control group patients did not. Patients assigned to the learning algorithm group experienced a superior reduction in blood glucose levels (glycated hemoglobin [HbA1c]) compared with control policies, and longer participation caused greater reductions in blood glucose levels. The learning algorithm improved gradually in predicting which messages would lead participants to exercise. Conclusions: Mobile phone apps coupled with a learning algorithm can improve adherence to exercise in diabetic patients. This algorithm can be used in large populations of diabetic patients to improve health and glycemic control. Our results can be expanded to other areas where computer-led health coaching of humans may have a positive impact. Summary of a part of this manuscript has been previously published as a letter in Diabetes Care, 2016.  [J Med Internet Res 2017;19(10):e338]

Connected through hyperedge HE-1 (induced by source paper "rapidly personalizing mobile health treatment policies with limited data"):
  with members:
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "on kernelized multi armed bandits"
    - "context aware bandits"
    - "a gang of bandits"
    - "sparse gaussian processes for multi task learning"
    - "information theoretic regret bounds for gaussian process optimization in the bandit setting"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "portfolio allocation for bayesian optimization"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning in healthcare a survey"):
  with members:
    - "does the artificial intelligence clinician learn optimal treatment strategies for sepsis in intensive care"
    - "a dual mode adaptive basal bolus advisor based on reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "improving sepsis treatment strategies by combining deep and kernel based reinforcement learning"
    - "partial policy based reinforcement learning for anatomical landmark localization in 3d medical images"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "automatic view planning with multi scale deep reinforcement learning agents"
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"

Connected through hyperedge HE-3 (induced by source paper "personalized heartsteps a reinforcement learning algorithm for optimizing physical activity"):
  with members:
    - "mitigating planner overfitting in model based reinforcement learning"
    - "a tutorial on thompson sampling"
    - "learning unknown markov decision processes a thompson sampling approach"
    - "misspecified linear bandits"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "how to discount deep reinforcement learning towards new dynamic strategies"
    - "learning to optimize via posterior sampling"

Connected through hyperedge HE-4 (induced by source paper "effect of values and technology use on exercise implications for personalized behavior change interventions"):
  with members:
    - "predicting demographics moral foundations and human values from digital behaviours"

Connected through hyperedge HE-5 (induced by source paper "personalization of health interventions using cluster based reinforcement learning"):
  with members:
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "deep reinforcement learning an overview"
    - "group driven reinforcement learning for personalized mhealth intervention"
    - "distral robust multitask reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "co morbidity exploration on wearables activity data using unsupervised pre training and multi task learning"):
  with members:
    - "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"
    - "sleep quality prediction from wearable data using deep learning"
    - "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.CY(Computers and Society)