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: "a machine learning analysis of the features in deceptive and credible news"
  Abstract: Fake news is a type of pervasive propaganda that spreads misinformation online, taking advantage of social media's extensive reach to manipulate public perception. Over the past three years, fake news has become a focal discussion point in the media due to its impact on the 2016 U.S. presidential election. Fake news can have severe real-world implications: in 2016, a man walked into a pizzeria carrying a rifle because he read that Hillary Clinton was harboring children as sex slaves. This project presents a high accuracy (87%) machine learning classifier that determines the validity of news based on the word distributions and specific linguistic and stylistic differences in the first few sentences of an article. This can help readers identify the validity of an article by looking for specific features in the opening lines aiding them in making informed decisions. Using a dataset of 2,107 articles from 30 different websites, this project establishes an understanding of the variations between fake and credible news by examining the model, dataset, and features. This classifier appears to use the differences in word distribution, levels of tone authenticity, and frequency of adverbs, adjectives, and nouns. The differentiation in the features of these articles can be used to improve future classifiers. This classifier can also be further applied directly to browsers as a Google Chrome extension or as a filter for social media outlets or news websites to reduce the spread of misinformation.

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.CL(Computation and Language)