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 "discourse in multimedia a case study in information extraction"):
  Abstract: To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features which can be leveraged for various NLP tasks. In this paper, we study some of these discourse features in multimedia text and what communicative function they fulfil in the context. We examine how these multimedia discourse features can be used to improve an information extraction system. We show that the discourse and text layout features provide information that is complementary to lexical semantic information commonly used for information extraction. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.

Includes members:
  Member "rhetorical relations for information retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dependencies formalising semantic catenae for information retrieval")
  Member "type and content driven synthesis of sql queries from natural language":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "semantic parsing with syntax and table aware sql generation")
  Member "a syntactic neural model for general purpose code generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploring neural models for parsing natural language into first order logic")
  Member "program induction by rationale generation learning to solve and explain algebraic word problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs")
  Member "latent predictor networks for code generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi modal attention network learning for semantic source code retrieval")
  Member "learning to map sentences to logical form structured classification with probabilistic categorial grammars":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "break it down a question understanding benchmark")
  Member "learning dependency based compositional semantics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "break it down a question understanding benchmark")
  Member "a pdtb styled end to end discourse parser":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discourse level factors for sentence deletion in text simplification")

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.CL(Computation and Language)