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: "using summarization to discover argument facets in online idealogical dialog"
  Abstract: More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue; what are the abstract objects under discussion that are central to a speaker’s argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.

Connected through hyperedge HE-1 (induced by source paper "samsum corpus a human annotated dialogue dataset for abstractive summarization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "sample efficient text summarization using a single pre trained transformer"
    - "pretraining based natural language generation for text summarization"
    - "abstractive summarization of spoken and written conversation"
    - "data driven summarization of scientific articles"
    - "abstractive dialogue summarization with sentence gated modeling optimized by dialogue acts"
    - "bottom up abstractive summarization"

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"
    - "the effect of wording on message propagation topic and author controlled natural experiments on twitter"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "contrastive reasons detection and clustering from online polarized debate"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "summarizing dialogic arguments from social media"
    - "stance and sentiment in tweets"
    - "scalable topical phrase mining from text corpora"
    - "lexrank graph based lexical centrality as salience in text summarization"

Connected through hyperedge HE-4 (induced by source paper "abstractive summarization of spoken and written conversation"):
  with members:
    - "get to the point summarization with pointer generator networks"
    - "modeling coverage for neural machine translation"
    - "abstractive meeting summarization usingdependency graph fusion"
    - "teaching machines to read and comprehend"

Connected through hyperedge HE-5 (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"
    - "measuring the similarity of sentential arguments in dialogue"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "what is the essence of a claim cross domain claim identification"):
  with members:
    - "argumentation mining in user generated web discourse"
    - "neural end to end learning for computational argumentation mining"
    - "parsing argumentation structures in persuasive essays"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification"
    - "unsupervised domain adaptation with feature embeddings"
    - "a pdtb styled end to end discourse parser"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-7 (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"
    - "measuring the similarity of sentential arguments in dialogue"
    - "neural network based abstract generation for opinions and arguments"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

Connected through hyperedge HE-8 (induced by source paper "social media argumentation mining the quest for deliberateness in raucousness"):
  with members:
    - "argumentation mining in user generated web discourse"

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.AI(Artificial Intelligence)