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: "winning on the merits the joint effects of content and style on debate outcomes"
  Abstract: Debate and deliberation play essential roles in politics and government, but most models presume that debates are won mainly via superior style or agenda control. Ideally, however, debates would be won on the merits, as a function of which side has the stronger arguments. We propose a predictive model of debate that estimates the effects of linguistic features and the latent persuasive strengths of different topics, as well as the interactions between the two. Using a dataset of 118 Oxford-style debates, our model's combination of content (as latent topics) and style (as linguistic features) allows us to predict audience-adjudicated winners with 74% accuracy, significantly outperforming linguistic features alone (66%). Our model finds that winning sides employ stronger arguments, and allows us to identify the linguistic features associated with strong or weak arguments.

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"
    - "attentive interaction model modeling changes in view in argumentation"
    - "joint modeling of content and discourse relations in dialogues"
    - "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 "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-4 (induced by source paper "finding your voice the linguistic development of mental health counselors"):
  with members:
    - "conversations gone awry detecting early signs of conversational failure"
    - "depression and self harm risk assessment in online forums"
    - "large scale analysis of counseling conversations an application of natural language processing to mental health"
    - "the social dynamics of language change in online networks"
    - "conversational markers of constructive discussions"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "computational sociolinguistics a survey"

Connected through hyperedge HE-5 (induced by source paper "argument mining for understanding peer reviews"):
  with members:
    - "deep contextualized word representations"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "argumentation mining in user generated web discourse"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "argument mining with structured svms and rnns"
    - "understanding and detecting supporting arguments of diverse types"
    - "parsing argumentation structures in persuasive essays"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "a corpus for modeling user and language effects in argumentation on online debating"):
  with members:
    - "exploring the role of prior beliefs for argument persuasion"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "talk it up or play it down un expected correlations between de emphasis and recurrence of discussion points in consequential u s economic policy meetings"
    - "conversational flow in oxford style debates"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "echoes of persuasion the effect of euphony in persuasive communication"
    - "a computational approach to politeness with application to social factors"

Connected through hyperedge HE-7 (induced by source paper "argument generation with retrieval planning and realization"):
  with members:
    - "bottom up abstractive summarization"
    - "neural argument generation augmented with externally retrieved evidence"
    - "a diversity promoting objective function for neural conversation models"
    - "neural machine translation by jointly learning to align and translate"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural argument generation augmented with externally retrieved evidence"):
  with members:
    - "neural end to end learning for computational argumentation mining"
    - "argument mining with structured svms and rnns"
    - "understanding and detecting supporting arguments of diverse types"
    - "sequence to sequence learning as beam search optimization"
    - "neural network based abstract generation for opinions and arguments"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"

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)