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 "optimized tracking of topic evolution"):
  Abstract: Topic evolution modeling has been researched for a long time and has gained considerable interest. A state-of-the-art method has been recently using word modeling algorithms in combination with community detection mechanisms to achieve better results in a more effective way. We analyse results of this approach and discuss the two major challenges that this approach still faces. Although the topics that have resulted from the recent algorithm are good in general, they are very noisy due to many topics that are very unimportant because of their size, words, or ambiguity. Additionally, the number of words defining each topic is too large, making it difficult to analyse them in their unsorted state. In this paper, we propose approaches to tackle these challenges by adding topic filtering and network analysis metrics to define the importance of a topic. We test different combinations of these metrics to see which combination yields the best results. Furthermore, we add word filtering and ranking to each topic to identify the words with the highest novelty automatically. We evaluate our enhancement methods in two ways: human qualitative evaluation and automatic quantitative evaluation. Moreover, we created two case studies to test the quality of the clusters and words. In the quantitative evaluation, we use the pairwise mutual information score to test the coherency of topics. The quantitative evaluation also includes an analysis of execution times for each part of the program. The results of the experimental evaluations show that the two evaluation methods agree on the positive feasibility of the algorithm. We then show possible extensions in the form of usability and future improvements to the algorithm.

Includes members:
  Member "public vs media opinion on robots":
    also connected through other hyperedges:
      (none)
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)