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 "personalized and situation aware multimodal route recommendations the favour algorithm"):
  Abstract: Route choice in multimodal networks shows a considerable variation between different individuals and the current situational context. Personalization and situation awareness of recommendation algorithms are already common in many areas, e.g., online retail. However, most online routing applications still provide shortest distance or shortest travel-time routes only, neglecting individual preferences, as well as the current situation. Both aspects are of particular importance in a multimodal setting as attractivity of some transportation modes, such as biking, which crucially depend on personal characteristics and exogenous factors, such as the weather. As an alternative, this paper introduces the FAVourite rOUte Recommendation (FAVOUR) approach to provide personalized, situation-aware route proposals based on three steps: first, at the initialization stage, the user provides limited information (home location, work place, mobility options, sociodemographics) used to select one out of a small number of initial profiles. Second, based on this information, a stated preference survey is designed in order to sharpen the profile. In this step, a mass preference prior (MPP) is used to encode the prior knowledge on preferences from the class identified in step one. Third, subsequently, the profile is continuously updated during usage of the routing services. The last two steps use Bayesian learning techniques in order to incorporate information from all contributing individuals. The FAVOUR approach is presented in detail and tested on a small number of survey participants. The experimental results on this real-world dataset show that FAVOUR generates better quality recommendations w.r.t. alternative learning algorithms from the literature. In particular, the definition of the MPP for initialization of step two is shown to provide better predictions than a number of alternatives from the literature.

Includes members:
  Member "crowdplanner a crowd based route recommendation system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient destination prediction based on route choices with transition matrix optimization")
  Member "preference elicitation for general random utility models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "eliciting single peaked preferences using comparison queries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "swap dynamics in single peaked house markets")

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.AI(Artificial Intelligence)