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 "a new keyphrases extraction method based on suffix tree data structure for arabic documents clustering"):
  Abstract: Document Clustering is a branch of a larger area of scientific study known as data mining .which is an unsupervised classification using to find a structure in a collection of unlabeled data. The useful information in the documents can be accompanied by a large amount of noise words when using Full Text Representation, and therefore will affect negatively the result of the clustering process. So it is with great need to eliminate the noise words and keeping just the useful information in order to enhance the quality of the clustering results. This problem occurs with different degree for any language such as English, European, Hindi, Chinese, and Arabic Language. To overcome this problem, in this paper, we propose a new and efficient Keyphrases extraction method based on the Suffix Tree data structure (KpST), the extracted Keyphrases are then used in the clustering process instead of Full Text Representation. The proposed method for Keyphrases extraction is language independent and therefore it may be applied to any language. In this investigation, we are interested to deal with the Arabic language which is one of the most complex languages. To evaluate our method, we conduct an experimental study on Arabic Documents using the most popular Clustering approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a variety of distance functions and similarity measures to perform Arabic Document Clustering task. The obtained results show that our method for extracting Keyphrases increases the quality of the clustering results. We propose also to study the effect of using the stemming for the testing dataset to cluster it with the same documents clustering techniques and similarity/distance measures.

Includes members:
  Member "arabic text summarization based on latent semantic analysis to enhance arabic documents clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an enhanced latent semantic analysis approach for arabic document summarization")
  Member "a comparative study of root based and stem based approaches for measuring the similarity between arabic words for arabic text mining applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clustering web search results for effective arabic language browsing")
  Member "kea practical automatic keyphrase extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "learning to extract keyphrases from text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "two huge title and keyword generation corpora of research articles")
  Member "extraction of keyphrases from text evaluation of four algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "arabic keyphrase extraction using linguistic knowledge and machine learning techniques")

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)