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: "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
  Abstract: Arguing without committing a fallacy is one of the main requirements of an ideal debate. But even when debating rules are strictly enforced and fallacious arguments punished, arguers often lapse into attacking the opponent by an ad hominem argument. As existing research lacks solid empirical investigation of the typology of ad hominem arguments as well as their potential causes, this paper fills this gap by (1) performing several large-scale annotation studies, (2) experimenting with various neural architectures and validating our working hypotheses, such as controversy or reasonableness, and (3) providing linguistic insights into triggers of ad hominem using explainable neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "is attention interpretable"):
  with members:
    - "attention is not explanation"
    - "interpreting recurrent and attention based neural models a case study on natural language inference"
    - "sanity checks for saliency maps"
    - "learning structured text representations"
    - "satirical news detection and analysis using attention mechanism and linguistic features"
    - "attention is all you need"
    - "neural discourse structure for text categorization"
    - "spine sparse interpretable neural embeddings"

Connected through hyperedge HE-2 (induced by source paper "findings of the nlp4if 2019 shared task on fine grained propaganda detection"):
  with members:
    - "fine tuned neural models for propaganda detection at the sentence and fragment levels"
    - "understanding bert performance in propaganda analysis"
    - "defending against neural fake news"
    - "neural architectures for fine grained propaganda detection in news"
    - "proppy a system to unmask propaganda in online news"
    - "fine grained analysis of propaganda in news articles"
    - "roberta a robustly optimized bert pretraining approach"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"

Connected through hyperedge HE-3 (induced by source paper "estimating causal effects of tone in online debates"):
  with members:
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "antisocial behavior in online discussion communities"
    - "emoticons vs emojis on twitter a causal inference approach"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "fine grained analysis of propaganda in news articles"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "argotario computational argumentation meets serious games"
    - "introduction to the conll 2003 shared task language independent named entity recognition"
    - "introduction to the conll 2002 shared task language independent named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "experiments in detecting persuasion techniques in the news"):
  with members:
    - "proppy a system to unmask propaganda in online news"
    - "fine grained analysis of propaganda in news articles"
    - "sampling the news producers a large news and feature data set for the study of the complex media landscape"

Connected through hyperedge HE-6 (induced by source paper "attentive interaction model modeling changes in view in argumentation"):
  with members:
    - "supervised learning of universal sentence representations from natural language inference data"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "argument mining with structured svms and rnns"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "personalized emphasis framing for persuasive message generation"
    - "a large annotated corpus for learning natural language inference"
    - "on the properties of neural machine translation encoder decoder approaches"

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)