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: "mark my words linguistic style accommodation in social media"
  Abstract: The psycholinguistic theory of communication accommodation accounts for the general observation that participants in conversations tend to converge to one another's communicative behavior: they coordinate in a variety of dimensions including choice of words, syntax, utterance length, pitch and gestures. In its almost forty years of existence, this theory has been empirically supported exclusively through small-scale or controlled laboratory studies. Here we address this phenomenon in the context of Twitter conversations. Undoubtedly, this setting is unlike any other in which accommodation was observed and, thus, challenging to the theory. Its novelty comes not only from its size, but also from the non real-time nature of conversations, from the 140 character length restriction, from the wide variety of social relation types, and from a design that was initially not geared towards conversation at all. Given such constraints, it is not clear a priori whether accommodation is robust enough to occur given the constraints of this new environment. To investigate this, we develop a probabilistic framework that can model accommodation and measure its effects. We apply it to a large Twitter conversational dataset specifically developed for this task. This is the first time the hypothesis of linguistic style accommodation has been examined (and verified) in a large scale, real world setting.   Furthermore, when investigating concepts such as stylistic influence and symmetry of accommodation, we discover a complexity of the phenomenon which was never observed before. We also explore the potential relation between stylistic influence and network features commonly associated with social status.

Connected through hyperedge HE-1 (induced by source paper "what gets echoed understanding the pointers in explanations of persuasive arguments"):
  with members:
    - "on human predictions with explanations and predictions of machine learning models a case study on deception detection"
    - "rating reliability and bias in news articles does ai assistance help everyone"
    - "explain yourself leveraging language models for commonsense reasoning"
    - "exploring the role of prior beliefs for argument persuasion"
    - "e snli natural language inference with natural language explanations"
    - "you are no jack kennedy on media selection of highlights from presidential debates"
    - "explanation in artificial intelligence insights from the social sciences"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "estimating causal effects of tone in online debates"):
  with members:
    - "before name calling dynamics and triggers of ad hominem fallacies in web argumentation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "modeling interpersonal linguistic coordination in conversations using word mover s distance"):
  with members:
    - "confusion2vec towards enriching vector space word representations with representational ambiguities"

Connected through hyperedge HE-4 (induced by source paper "semstyle learning to generate stylised image captions using unaligned text"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "controlling linguistic style aspects in neural language generation"
    - "areas of attention for image captioning"
    - "spice semantic propositional image caption evaluation"
    - "boosting image captioning with attributes"
    - "improving lstm based video description with linguistic knowledge mined from text"
    - "phi lstm a phrase based hierarchical lstm model for image captioning"

Connected through hyperedge HE-5 (induced by source paper "the rllchatbot a solution to the convai challenge"):
  with members:
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a deep reinforcement learning chatbot"
    - "ethical challenges in data driven dialogue systems"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-6 (induced by source paper "a survey of hierarchy identification in social networks"):
  with members:
    - "rankdcg rank ordering evaluation measure"
    - "conversation modeling on reddit using a graph structured lstm"
    - "learning convolutional neural networks for graphs"
    - "stacked attention networks for image question answering"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "a computational approach to politeness with application to social factors"
    - "hedge detection as a lens on framing in the gmo debates a position paper"

Connected through hyperedge HE-7 (induced by source paper "loyalty in online communities"):
  with members:
    - "community identity and user engagement in a multi community landscape"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "characterizing and curating conversation threads expansion focus volume re entry"

Connected through hyperedge HE-8 (induced by source paper "tweeting ai perceptions of ai tweeters ait vs expert ai tweeters eait"):
  with members:
    - "tweeting the mind and instagramming the heart exploring differentiated content sharing on social media"
    - "efficient estimation of word representations in vector space"

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)