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: "exploring the role of prior beliefs for argument persuasion"
  Abstract: Public debate forums provide a common platform for exchanging opinions on a topic of interest. While recent studies in natural language processing (NLP) have provided empirical evidence that the language of the debaters and their patterns of interaction play a key role in changing the mind of a reader, research in psychology has shown that prior beliefs can affect our interpretation of an argument and could therefore constitute a competing alternative explanation for resistance to changing one's stance. To study the actual effect of language use vs. prior beliefs on persuasion, we provide a new dataset and propose a controlled setting that takes into consideration two reader level factors: political and religious ideology. We find that prior beliefs affected by these reader level factors play a more important role than language use effects and argue that it is important to account for them in NLP studies of persuasion.

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"
    - "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"
    - "winning on the merits the joint effects of content and style on debate outcomes"

Connected through hyperedge HE-2 (induced by source paper "determining relative argument specificity and stance for complex argumentative structures"):
  with members:
    - "domain agnostic real valued specificity prediction"
    - "neural argument generation augmented with externally retrieved evidence"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "predicting specificity in classroom discussion"
    - "debbie the debate bot of the future"
    - "argument mining with structured svms and rnns"
    - "neural network based abstract generation for opinions and arguments"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-3 (induced by source paper "are you convinced choosing the more convincing evidence with a siamese network"):
  with members:
    - "seeing things from a different angle discovering diverse perspectives about claims"
    - "finding convincing arguments using scalable bayesian preference learning"
    - "deep contextualized word representations"
    - "parsing argumentation structures in persuasive essays"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "recurrent batch normalization"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a corpus for modeling user and language effects in argumentation on online debating"):
  with members:
    - "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"
    - "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"

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)