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: "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
  Abstract: We investigate the characteristics of factual and emotional argumentation styles observed in online debates. Using an annotated set of FACTUAL and FEELING debate forum posts, we extract patterns that are highly correlated with factual and emotional arguments, and then apply a bootstrapping methodology to find new patterns in a larger pool of unannotated forum posts. This process automatically produces a large set of patterns representing linguistic expressions that are highly correlated with factual and emotional language. Finally, we analyze the most discriminating patterns to better understand the defining characteristics of factual and emotional arguments.

Connected through hyperedge HE-1 (induced by source paper "human misinformation interaction understanding the interdisciplinary approach needed to computationally combat false information"):
  with members:
    - "the spread of low credibility content by social bots"
    - "liar liar pants on fire a new benchmark dataset for fake news detection"
    - "the spread of fake news by social bots"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "fake news detection on social media a data mining perspective"
    - "automated assistants to identify and prompt action on visual news bias"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"

Connected through hyperedge HE-2 (induced by source paper "data driven dialogue systems for social agents"):
  with members:
    - "automating direct speech variations in stories and games"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "m2d monolog to dialog generation for conversational story telling"
    - "a persona based neural conversation model"
    - "measuring the similarity of sentential arguments in dialogue"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"

Connected through hyperedge HE-3 (induced by source paper "debbie the debate bot of the future"):
  with members:
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "m2d monolog to dialog generation for conversational story telling"
    - "measuring the similarity of sentential arguments in dialogue"
    - "neural network based abstract generation for opinions and arguments"
    - "using summarization to discover argument facets in online idealogical dialog"

Connected through hyperedge HE-4 (induced by source paper "argument strength is in the eye of the beholder audience effects in persuasion"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "measuring the similarity of sentential arguments in dialogue"
    - "learning latent local conversation modes for predicting community endorsement in online discussions"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"

Connected through hyperedge HE-5 (induced by source paper "harvesting creative templates for generating stylistically varied restaurant reviews"):
  with members:
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "crowd sourcing nlg data pictures elicit better data"
    - "semantically conditioned lstm based natural language generation for spoken dialogue systems"
    - "individual and domain adaptation in sentence planning for dialogue"

Connected through hyperedge HE-6 (induced by source paper "are you serious rhetorical questions and sarcasm in social media dialog"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "creating and characterizing a diverse corpus of sarcasm in dialogue"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "visualizing and understanding neural models in nlp"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "linguistic reflexes of well being and happiness in echo"):
  with members:
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"

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)