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 "fast and accurate annotation of short texts with wikipedia pages"):
  Abstract: We address the problem of cross-referencing text fragments with Wikipedia pages, in a way that synonymy and polysemy issues are resolved accurately and efficiently. We take inspiration from a recent flow of work [Cucerzan 2007, Mihalcea and Csomai 2007, Milne and Witten 2008, Chakrabarti et al 2009], and extend their scenario from the annotation of long documents to the annotation of short texts, such as snippets of search-engine results, tweets, news, blogs, etc.. These short and poorly composed texts pose new challenges in terms of efficiency and effectiveness of the annotation process, that we address by designing and engineering TAGME, the first system that performs an accurate and on-the-fly annotation of these short textual fragments. A large set of experiments shows that TAGME outperforms state-of-the-art algorithms when they are adapted to work on short texts and it results fast and competitive on long texts.

Includes members:
  Member "tagme on the fly annotation of short text fragments by wikipedia entities":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards quantifying the distance between opinions")
  Member "wikipedia based semantic interpretation for natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "eliminating search intent bias in learning to rank")
  Member "mining meaning from wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "alter auxiliary text rewriting tool for natural language generation")

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)