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 "estimating causal effects of tone in online debates"):
  Abstract: Statistical methods applied to social media posts shed light on the dynamics of online dialogue. For example, users' wording choices predict their persuasiveness and users adopt the language patterns of other dialogue participants. In this paper, we estimate the causal effect of reply tones in debates on linguistic and sentiment changes in subsequent responses. The challenge for this estimation is that a reply's tone and subsequent responses are confounded by the users' ideologies on the debate topic and their emotions. To overcome this challenge, we learn representations of ideology using generative models of text. We study debates from 4Forums and compare annotated tones of replying such as emotional versus factual, or reasonable versus attacking. We show that our latent confounder representation reduces bias in ATE estimation. Our results suggest that factual and asserting tones affect dialogue and provide a methodology for estimating causal effects from text.

Includes members:
  Member "before name calling dynamics and triggers of ad hominem fallacies in web argumentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "is attention interpretable")
  Member "topic independent identification of agreement and disagreement in social media dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recognising agreement and disagreement between stances with reason comparing networks")
  Member "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection")
  Member "winning arguments interaction dynamics and persuasion strategies in good faith online discussions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ten social dimensions of conversations and relationships")
  Member "antisocial behavior in online discussion communities":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "empirical analysis of multi task learning for reducing model bias in toxic comment detection")
  Member "emoticons vs emojis on twitter a causal inference approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "i stand with you using emojis to study solidarity in crisis events")
  Member "using summarization to discover argument facets in online idealogical dialog":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "samsum corpus a human annotated dialogue dataset for abstractive summarization")
  Member "the effect of wording on message propagation topic and author controlled natural experiments on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "why is chicago deceptive towards building model driven tutorials for humans")

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)