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: "is this a good title"
  Abstract: Missing web pages, URIs that return the 404 "Page Not Found" error or the HTTP response code 200 but dereference unexpected content, are ubiquitous in today's browsing experience. We use Internet search engines to relocate such missing pages and provide means that help automate the rediscovery process. We propose querying web pages' titles against search engines. We investigate the retrieval performance of titles and compare them to lexical signatures which are derived from the pages' content. Since titles naturally represent the content of a document they intuitively change over time. We measure the edit distance between current titles and titles of copies of the same pages obtained from the Internet Archive and display their evolution. We further investigate the correlation between title changes and content modifications of a web page over time. Lastly we provide a predictive model for the quality of any given web page title in terms of its discovery performance. Our results show that titles return more than 60% URIs top ranked and further relevant content returned in the top 10 results. We show that titles decay slowly but are far more stable than the pages' content. We further distill stop titles than can help identify insufficiently performing search engine queries.

Connected through hyperedge HE-1 (induced by source paper "reading the correct history modeling temporal intention in resource sharing"):
  with members:
    - "losing my revolution how many resources shared on social media have been lost"
    - "analyzing the persistence of referenced web resources with memento"
    - "twitter mood predicts the stock market"
    - "memento time travel for the web"
    - "nltk the natural language toolkit"

Connected through hyperedge HE-2 (induced by source paper "arclink optimization techniques to build and retrieve the temporal web graph"):
  with members:
    - "access patterns for robots and humans in web archives"
    - "how much of the web is archived"
    - "characterization of search engine caches"

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)