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: "how gamification affects physical activity large scale analysis of walking challenges in a mobile application"
  Abstract: Gamification represents an effective way to incentivize user behavior across a number of computing applications. However, despite the fact that physical activity is essential for a healthy lifestyle, surprisingly little is known about how gamification and in particular competitions shape human physical activity. Here we study how competitions affect physical activity. We focus on walking challenges in a mobile activity tracking application where multiple users compete over who takes the most steps over a predefined number of days. We synthesize our findings in a series of game and app design implications. In particular, we analyze nearly 2,500 physical activity competitions over a period of one year capturing more than 800,000 person days of activity tracking. We observe that during walking competitions, the average user increases physical activity by 23%. Furthermore, there are large increases in activity for both men and women across all ages, and weight status, and even for users that were previously fairly inactive. We also find that the composition of participants greatly affects the dynamics of the game. In particular, if highly unequal participants get matched to each other, then competition suffers and the overall effect on the physical activity drops significantly. Furthermore, competitions with an equal mix of both men and women are more effective in increasing the level of activities. We leverage these insights to develop a statistical model to predict whether or not a competition will be particularly engaging with significant accuracy. Our models can serve as a guideline to help design more engaging competitions that lead to most beneficial behavioral changes.

Connected through hyperedge HE-1 (induced by source paper "modeling interdependent and periodic real world action sequences"):
  with members:
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "portrait of an online shopper understanding and predicting consumer behavior"
    - "modeling adoption and usage of competing products"

Connected through hyperedge HE-2 (induced by source paper "i ll be back on the multiple lives of users of a mobile activity tracking application"):
  with members:
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "donor retention in online crowdfunding communities a case study of donorschoose org"
    - "wearable computing for health and fitness exploring the relationship between data and human behaviour"
    - "engaging with massive online courses"

Connected through hyperedge HE-3 (induced by source paper "hearts and politics metrics for tracking biorhythm changes during brexit and trump"):
  with members:
    - "quantified self meets social media sharing of weight updates on twitter"

Connected through hyperedge HE-4 (induced by source paper "harnessing the web for population scale physiological sensing a case study of sleep and performance"):
  with members:
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "online actions with offline impact how online social networks influence online and offline user behavior"
    - "from cookies to cooks insights on dietary patterns via analysis of web usage logs"

Connected through hyperedge HE-5 (induced by source paper "modeling individual cyclic variation in human behavior"):
  with members:
    - "harnessing the web for population scale physiological sensing a case study of sleep and performance"
    - "pomegranate fast and flexible probabilistic modeling in python"
    - "online actions with offline impact how online social networks influence online and offline user behavior"

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)