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 "mudos ng multi document summaries using n gram graphs tech report"):
  Abstract: This report describes the MUDOS-NG summarization system, which applies a set of language-independent and generic methods for generating extractive summaries. The proposed methods are mostly combinations of simple operators on a generic character n-gram graph representation of texts. This work defines the set of used operators upon n-gram graphs and proposes using these operators within the multi-document summarization process in such subtasks as document analysis, salient sentence selection, query expansion and redundancy control. Furthermore, a novel chunking methodology is used, together with a novel way to assign concepts to sentences for query expansion. The experimental results of the summarization system, performed upon widely used corpora from the Document Understanding and the Text Analysis Conferences, are promising and provide evidence for the potential of the generic methods introduced. This work aims to designate core methods exploiting the n-gram graph representation, providing the basis for more advanced summarization systems.

Includes members:
  Member "lexrank graph based lexical centrality as salience in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating representative headlines for news stories")
  Member "inferring strategies for sentence ordering in multidocument news summarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what is this article about extreme summarization with topic aware convolutional neural networks")
  Member "centroid based summarization of multiple documents sentence extraction utility based evaluation and user studies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi news a large scale multi document summarization dataset and abstractive hierarchical model")

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)