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: "measuring the similarity of sentential arguments in dialogue"
  Abstract: When people converse about social or political topics, similar arguments are often paraphrased by different speakers, across many different conversations. Debate websites produce curated summaries of arguments on such topics; these summaries typically consist of lists of sentences that represent frequently paraphrased propositions, or labels capturing the essence of one particular aspect of an argument, e.g. Morality or Second Amendment. We call these frequently paraphrased propositions ARGUMENT FACETS. Like these curated sites, our goal is to induce and identify argument facets across multiple conversations, and produce summaries. However, we aim to do this automatically. We frame the problem as consisting of two steps: we first extract sentences that express an argument from raw social media dialogs, and then rank the extracted arguments in terms of their similarity to one another. Sets of similar arguments are used to represent argument facets. We show here that we can predict ARGUMENT FACET SIMILARITY with a correlation averaging 0.63 compared to a human topline averaging 0.68 over three debate topics, easily beating several reasonable baselines.

Connected through hyperedge HE-1 (induced by source paper "sentence bert sentence embeddings using siamese bert networks"):
  with members:
    - "classification and clustering of arguments with contextualized word embeddings"
    - "on measuring social biases in sentence encoders"
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "bertscore evaluating text generation with bert"
    - "understanding the behaviors of bert in ranking"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "learning semantic textual similarity from conversations"

Connected through hyperedge HE-2 (induced by source paper "classification and clustering of arguments with contextualized word embeddings"):
  with members:
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "neural end to end learning for computational argumentation mining"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "data driven dialogue systems for social agents"):
  with members:
    - "automating direct speech variations in stories and games"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "m2d monolog to dialog generation for conversational story telling"
    - "a persona based neural conversation model"
    - "improved deep learning baselines for ubuntu corpus dialogs"
    - "a survey of available corpora for building data driven dialogue systems"
    - "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

Connected through hyperedge HE-4 (induced by source paper "debbie the debate bot of the future"):
  with members:
    - "topic independent identification of agreement and disagreement in social media dialogue"
    - "argument strength is in the eye of the beholder audience effects in persuasion"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "m2d monolog to dialog generation for conversational story telling"
    - "neural network based abstract generation for opinions and arguments"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
    - "using summarization to discover argument facets in online idealogical dialog"

Connected through hyperedge HE-5 (induced by source paper "argument strength is in the eye of the beholder audience effects in persuasion"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "learning latent local conversation modes for predicting community endorsement in online discussions"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

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)