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: "rhetorical relations for information retrieval"
  Abstract: Typically, every part in most coherent text has some plausible reason for its presence, some function that it performs to the overall semantics of the text. Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts of a text are linked to each other. Knowledge about this socalled discourse structure has been applied successfully to several natural language processing tasks. This work studies the use of rhetorical relations for Information Retrieval (IR): Is there a correlation between certain rhetorical relations and retrieval performance? Can knowledge about a document's rhetorical relations be useful to IR? We present a language model modification that considers rhetorical relations when estimating the relevance of a document to a query. Empirical evaluation of different versions of our model on TREC settings shows that certain rhetorical relations can benefit retrieval effectiveness notably (> 10% in mean average precision over a state-of-the-art baseline).

Connected through hyperedge HE-1 (induced by source paper "discourse in multimedia a case study in information extraction"):
  with members:
    - "type and content driven synthesis of sql queries from natural language"
    - "a syntactic neural model for general purpose code generation"
    - "program induction by rationale generation learning to solve and explain algebraic word problems"
    - "latent predictor networks for code generation"
    - "learning to map sentences to logical form structured classification with probabilistic categorial grammars"
    - "learning dependency based compositional semantics"
    - "a pdtb styled end to end discourse parser"
    - "pattern matching and discourse processing in information extraction from japanese text"

Connected through hyperedge HE-2 (induced by source paper "dependencies formalising semantic catenae for information retrieval"):
  with members:
    - "fixed versus dynamic co occurrence windows in textrank term weights for information retrieval"
    - "preliminary experiments using subjective logic for the polyrepresentation of information needs"
    - "time series adaptive estimation of vaccination uptake using web search queries"
    - "part of speech based term weighting for information retrieval"
    - "learning to generate compositional color descriptions"
    - "adaptive distributional extensions to dfr ranking"
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"

Connected through hyperedge HE-3 (induced by source paper "a study of metrics of distance and correlation between ranked lists for compositionality detection"):
  with members:
    - "part of speech based term weighting for information retrieval"
    - "maximal rank correlation"
    - "maximal rank correlation"
    - "non compositional term dependence for information retrieval"
    - "a weighted correlation index for rankings with ties"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "a study of factuality objectivity and relevance three desiderata in large scale information retrieval"):
  with members:
    - "exploiting the bipartite structure of entity grids for document coherence and retrieval"
    - "multi dueling bandits and their application to online ranker evaluation"
    - "an improved multileaving algorithm for online ranker evaluation"
    - "entropy and graph based modelling of document coherence using discourse entities an application"
    - "non compositional term dependence for information retrieval"
    - "efficient and effective spam filtering and re ranking for large web datasets"

Connected through hyperedge HE-5 (induced by source paper "exploiting the bipartite structure of entity grids for document coherence and retrieval"):
  with members:
    - "entropy and graph based modelling of document coherence using discourse entities an application"
    - "efficient and effective spam filtering and re ranking for large web datasets"
    - "catching the drift probabilistic content models with applications to generation and summarization"

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)