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: "corpus structure language models and ad hoc information retrieval"
  Abstract: Most previous work on the recently developed language-modeling approach to information retrieval focuses on document-specific characteristics, and therefore does not take into account the structure of the surrounding corpus. We propose a novel algorithmic framework in which information provided by document-based language models is enhanced by the incorporation of information drawn from clusters of similar documents. Using this framework, we develop a suite of new algorithms. Even the simplest typically outperforms the standard language-modeling approach in precision and recall, and our new interpolation algorithm posts statistically significant improvements for both metrics over all three corpora tested.

Connected through hyperedge HE-1 (induced by source paper "related fact checks a tool for combating fake news"):
  with members:
    - "some like it hoax automated fake news detection in social networks"

Connected through hyperedge HE-2 (induced by source paper "respect my authority hits without hyperlinks utilizing cluster based language models"):
  with members:
    - "better than the real thing iterative pseudo query processing using cluster based language models"
    - "pagerank without hyperlinks structural re ranking using links induced by language models"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-3 (induced by source paper "pagerank without hyperlinks structural re ranking using links induced by language models"):
  with members:
    - "better than the real thing iterative pseudo query processing using cluster based language models"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-4 (induced by source paper "better than the real thing iterative pseudo query processing using cluster based language models"):
  with members:
    - "pagerank without hyperlinks structural re ranking using links induced by language models"

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)