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: "attentive interaction model modeling changes in view in argumentation"
  Abstract: We present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder's (OH's) reasoning and a challenger's argument, with the goal of predicting if the argument successfully changes the OH's view. The model has two components: (1) vulnerable region detection, an attention model that identifies parts of the OH's reasoning that are amenable to change, and (2) interaction encoding, which identifies the relationship between the content of the OH's reasoning and that of the challenger's argument. Based on evaluation on discussions from the Change My View forum on Reddit, the two components work together to predict an OH's change in view, outperforming several baselines. A posthoc analysis suggests that sentences picked out by the attention model are addressed more frequently by successful arguments than by unsuccessful ones.

Connected through hyperedge HE-1 (induced by source paper "what changed your mind the roles of dynamic topics and discourse in argumentation process"):
  with members:
    - "unsupervised dialog structure learning"
    - "what you say and how you say it joint modeling of topics and discourse in microblog conversations"
    - "what gets echoed understanding the pointers in explanations of persuasive arguments"
    - "topic memory networks for short text classification"
    - "a joint model of conversational discourse and latent topics on microblogs"
    - "joint modeling of content and discourse relations in dialogues"
    - "winning on the merits the joint effects of content and style on debate outcomes"
    - "argument strength is in the eye of the beholder audience effects in persuasion"

Connected through hyperedge HE-2 (induced by source paper "trouble on the horizon forecasting the derailment of online conversations as they develop"):
  with members:
    - "something s brewing early prediction of controversy causing posts from discussion features"
    - "forecasting the presence and intensity of hostility on instagram using linguistic and social features"
    - "the effect of extremist violence on hateful speech online"
    - "deep contextualized word representations"
    - "community interaction and conflict on the web"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"

Connected through hyperedge HE-3 (induced by source paper "modeling financial analysts decision making via the pragmatics and semantics of earnings calls"):
  with members:
    - "asking too much the rhetorical role of questions in political discourse"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "relnet end to end modeling of entities relations"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "leverage financial news to predict stock price movements using word embeddings and deep neural networks"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"

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)