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: "pagerank without hyperlinks structural re ranking using links induced by language models"
  Abstract: Inspired by the PageRank and HITS (hubs and authorities) algorithms for Web search, we propose a structural re-ranking approach to ad hoc information retrieval: we reorder the documents in an initially retrieved set by exploiting asymmetric relationships between them. Specifically, we consider generation links, which indicate that the language model induced from one document assigns high probability to the text of another; in doing so, we take care to prevent bias against long documents. We study a number of re-ranking criteria based on measures of centrality in the graphs formed by generation links, and show that integrating centrality into standard language-model-based retrieval is quite effective at improving precision at top ranks.

Connected through hyperedge HE-1 (induced by source paper "two stage document length normalization for information retrieval"):
  with members:
    - "improving term frequency normalization for multi topical documents and application to language modeling approaches"

Connected through hyperedge HE-2 (induced by source paper "centrality as relevance support sets and similarity as geometric proximity"):
  with members:
    - "from frequency to meaning vector space models of semantics"
    - "sentence compression as tree transduction"
    - "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies"

Connected through hyperedge HE-3 (induced by source paper "from identical to similar fusing retrieved lists based on inter document similarities"):
  with members:
    - "respect my authority hits without hyperlinks utilizing cluster based language models"
    - "probfuse a probabilistic approach to data fusion"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (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"
    - "lexrank graph based lexical centrality as salience in text summarization"
    - "corpus structure language models and ad hoc information retrieval"

Connected through hyperedge HE-5 (induced by source paper "better than the real thing iterative pseudo query processing using cluster based language models"):
  with members:
    - "corpus structure language models and ad hoc information retrieval"

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)