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 "iterative expectation for multi period information retrieval"):
  Abstract: Many Information Retrieval (IR) models make use of offline statistical techniques to score documents for ranking over a single period, rather than use an online, dynamic system that is responsive to users over time. In this paper, we explicitly formulate a general Multi Period Information Retrieval problem, where we consider retrieval as a stochastic yet controllable process. The ranking action during the process continuously controls the retrieval system's dynamics, and an optimal ranking policy is found in order to maximise the overall users' satisfaction over the multiple periods as much as possible. Our derivations show interesting properties about how the posterior probability of the documents relevancy evolves from users feedbacks through clicks, and provides a plug-in framework for incorporating different click models. Based on the Multi-Armed Bandit theory, we propose a simple implementation of our framework using a dynamic ranking rule that takes rank bias and exploration of documents into account. We use TREC data to learn a suitable exploration parameter for our model, and then analyse its performance and a number of variants using a search log data set; the experiments suggest an ability to explore document relevance dynamically over time using user feedback in a way that can handle rank bias.

Includes members:
  Member "online learning with preference feedback":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "conquer confusion queried online bandit learning")
  Member "pagerank standing on the shoulders of giants":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trust evaluation mechanism for user recruitment in mobile crowd sensing in the internet of things")

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.IR(Information Retrieval)