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 "on the estimation and use of statistical modelling in information retrieval"):
  Abstract: Several tasks in information retrieval (IR) rely on assumptions regarding the distribution of some property (such as term frequency) in the data being processed. This thesis argues that such distributional assumptions can lead to incorrect conclusions and proposes a statistically principled method for determining the "true" distribution. This thesis further applies this method to derive a new family of ranking models that adapt their computations to the statistics of the data being processed. Experimental evaluation shows results on par or better than multiple strong baselines on several TREC collections. Overall, this thesis concludes that distributional assumptions can be replaced with an effective, efficient and principled method for determining the "true" distribution and that using the "true" distribution can lead to improved retrieval performance.

Includes members:
  Member "part of speech based term weighting for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dependencies formalising semantic catenae for information retrieval")
  Member "social resilience in online communities the autopsy of friendster":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "span core decomposition for temporal networks algorithms and applications")
  Member "vocabulary growth in collaborative tagging systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how the nature of web services drives vocabulary creation in social tagging")
  Member "collaborative thesaurus tagging the wikipedia way":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality")
  Member "the structure of broad topics on the web":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning visual features from snapshots for web search")
  Member "search in power law networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a general centrality framework based on node navigability")

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)