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: "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality"
  Abstract: This paper aims to review the fiercely discussed question of whether the ranking of Wikipedia articles in search engines is justified by the quality of the articles. After an overview of current research on information quality in Wikipedia, a summary of the extended discussion on the quality of encyclopedic entries in general is given. On this basis, a heuristic method for evaluating Wikipedia entries is developed and applied to Wikipedia articles that scored highly in a search engine retrieval effectiveness test and compared with the relevance judgment of jurors. In all search engines tested, Wikipedia results are unanimously judged better by the jurors than other results on the corresponding results position. Relevance judgments often roughly correspond with the results from the heuristic evaluation. Cases in which high relevance judgments are not in accordance with the comparatively low score from the heuristic evaluation are interpreted as an indicator of a high degree of trust in Wikipedia. One of the systemic shortcomings of Wikipedia lies in its necessarily incoherent user model. A further tuning of the suggested criteria catalog, for instance, the different weighing of the supplied criteria, could serve as a starting point for a user model differentiated evaluation of Wikipedia articles. Approved methods of quality evaluation of reference works are applied to Wikipedia articles and integrated with the question of search engine evaluation. © 2011 Wiley Periodicals, Inc.

Connected through hyperedge HE-1 (induced by source paper "wikilinkgraphs a complete longitudinal and multi language dataset of the wikipedia link networks"):
  with members:
    - "benchmarking distributed stream processing engines"
    - "growing wikipedia across languages via recommendation"
    - "mining missing hyperlinks from human navigation traces a case study of wikipedia"
    - "societal controversies in wikipedia articles"
    - "interactions of cultures and top people of wikipedia from ranking of 24 language editions"
    - "biographical social networks on wikipedia a cross cultural study of links that made history"
    - "pagerank standing on the shoulders of giants"

Connected through hyperedge HE-2 (induced by source paper "recognizing descriptive wikipedia categories for historical figures"):
  with members:
    - "deepwalk online learning of social representations"
    - "entity ranking in wikipedia"

Connected through hyperedge HE-3 (induced by source paper "understanding editing behaviors in multilingual wikipedia"):
  with members:
    - "cross language wikipedia editing of okinawa japan"
    - "multilinguals and wikipedia editing"
    - "a practical approach to language complexity a wikipedia case study"
    - "the tower of babel meets web 2 0 user generated content and its applications in a multilingual context"

Connected through hyperedge HE-4 (induced by source paper "evaluating the retrieval effectiveness of web search engines using a representative query sample"):
  with members:
    - "the influence of commercial intent of search results on their perceived relevance"
    - "the retrieval effectiveness of search engines on navigational queries"
    - "the retrieval effectiveness of web search engines considering results descriptions"
    - "credibility in web search engines"

Connected through hyperedge HE-5 (induced by source paper "credibility in web search engines"):
  with members:
    - "what users see structures in search engine results pages"
    - "the retrieval effectiveness of web search engines considering results descriptions"
    - "a framework for evaluating the retrieval effectiveness of search engines"

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.IR(Information Retrieval)