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: "the retrieval effectiveness of web search engines considering results descriptions"
  Abstract: Purpose: To compare five major Web search engines (Google, Yahoo, MSN, Ask.com, and Seekport) for their retrieval effectiveness, taking into account not only the results but also the results descriptions. #R##N#Design/Methodology/Approach: The study uses real-life queries. Results are made anonymous and are randomised. Results are judged by the persons posing the original queries. #R##N#Findings: The two major search engines, Google and Yahoo, perform best, and there are no significant differences between them. Google delivers significantly more relevant result descriptions than any other search engine. This could be one reason for users perceiving this engine as superior. #R##N#Research Limitations: The study is based on a user model where the user takes into account a certain amount of results rather systematically. This may not be the case in real life. #R##N#Practical Implications: Implies that search engines should focus on relevant descriptions. Searchers are advised to use other search engines in addition to Google. #R##N#Originality/Value: This is the first major study comparing results and descriptions systematically and proposes new retrieval measures to take into account results descriptions

Connected through hyperedge HE-1 (induced by source paper "featured snippets results in google web search an exploratory study"):
  with members:
    - "the snippets taxonomy in web search engines"
    - "generating titles for web tables"

Connected through hyperedge HE-2 (induced by source paper "the snippets taxonomy in web search engines"):
  with members:
    - "generating titles for web tables"

Connected through hyperedge HE-3 (induced by source paper "what users see structures in search engine results pages"):
  with members:
    - "problems with the use of web search engines to find results in foreign languages"
    - "the retrieval effectiveness of search engines on navigational queries"

Connected through hyperedge HE-4 (induced by source paper "the influence of commercial intent of search results on their perceived relevance"):
  with members:
    - "the retrieval effectiveness of search engines on navigational queries"

Connected through hyperedge HE-5 (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"
    - "credibility in web search engines"
    - "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality"

Connected through hyperedge HE-6 (induced by source paper "on search engine evaluation metrics"):
  with members:
    - "predicting user preferences"
    - "model adaptation via model interpolation and boosting for web search ranking"
    - "minimally invasive randomization for collecting unbiased preferences from clickthrough logs"

Connected through hyperedge HE-7 (induced by source paper "a framework for evaluating the retrieval effectiveness of search engines"):
  with members:
    - "the influence of commercial intent of search results on their perceived relevance"
    - "problems with the use of web search engines to find results in foreign languages"
    - "the retrieval effectiveness of search engines on navigational queries"
    - "implications of inter rater agreement on a student information retrieval evaluation"

Connected through hyperedge HE-8 (induced by source paper "credibility in web search engines"):
  with members:
    - "what users see structures in search engine results pages"
    - "a framework for evaluating the retrieval effectiveness of search engines"
    - "ranking of wikipedia articles in search engines revisited fair ranking for reasonable quality"

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)