human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "modeling interdependent and periodic real world action sequences"):
  Abstract: Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions in the real world is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model, called TIPAS, for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million real-world actions (e.g., eating, sleep, and exercise) taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, TIPAS improves predictions of actions, and their timing, over existing methods across two datasets by up to 156% and 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.

Includes members:
  Member "how gamification affects physical activity large scale analysis of walking challenges in a mobile application":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "i ll be back on the multiple lives of users of a mobile activity tracking application")
  Member "anyone can become a troll causes of trolling behavior in online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "harnessing the web for population scale physiological sensing a case study of sleep and performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capturing evolution genes for time series data")
  Member "online actions with offline impact how online social networks influence online and offline user behavior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unlocking social media and user generated content as a data source for knowledge management")
  Member "coevolve a joint point process model for information diffusion and network co evolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  Member "portrait of an online shopper understanding and predicting consumer behavior":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "when your friends become sellers an empirical study of social commerce site beidian")
  Member "modeling adoption and usage of competing products":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural jump stochastic differential equations")

Question: Please tell me which class the source paper 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.SI(Social and Information Networks)