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 "why do urban legends go viral"):
  Abstract: Urban legends are viral deceptive texts, in between credible and incredible.To be credible they mimic news articles while being incredible like a fairy tale.High level features: "who/where/when" of news, "emotional/readable" of fairy tales.Quantitative analysis and machine learning experiments for recognizing urban legends. Urban legends are a genre of modern folklore, consisting of stories about rare and exceptional events, just plausible enough to be believed, which tend to propagate inexorably across communities. In our view, while urban legends represent a form of "sticky" deceptive text, they are marked by a tension between the credible and incredible. They should be credible like a news article and incredible like a fairy tale to go viral. In particular we will focus on the idea that urban legends should mimic the details of news (who, where, when) to be credible, while they should be emotional and readable like a fairy tale to be catchy and memorable. Using NLP tools we will provide a quantitative analysis of these prototypical characteristics. We also lay out some machine learning experiments showing that it is possible to recognize an urban legend using just these simple features.

Includes members:
  Member "exploring text virality in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal effects of brevity on style and success in social media")
  Member "assessing sentiment strength in words prior polarities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sentiment analysis how to derive prior polarities from sentiwordnet")
  Member "do linguistic style and readability of scientific abstracts affect their virality":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language processing for mental health large scale discourse analysis of counseling conversations")
  Member "you had me at hello how phrasing affects memorability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "movie plot analysis via turning point identification")
  Member "finding deceptive opinion spam by any stretch of the imagination":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "description based text classification with reinforcement learning")
  Member "information contagion an empirical study of the spread of news on digg and twitter social networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling information diffusion in online social networks with partial differential equations")
  Member "analysis of social voting patterns on digg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "popularity prediction on social platforms with coupled graph neural networks")
  Member "more accurate tests for the statistical significance of result differences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two level transformer and auxiliary coherence modeling for improved text segmentation")

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)