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: "topic independent identification of agreement and disagreement in social media dialogue"
  Abstract: Research on the structure of dialogue has been hampered for years because large dialogue corpora have not been available. This has impacted the dialogue research community's ability to develop better theories, as well as good off the shelf tools for dialogue processing. Happily, an increasing amount of information and opinion exchange occur in natural dialogue in online forums, where people share their opinions about a vast range of topics. In particular we are interested in rejection in dialogue, also called disagreement and denial, where the size of available dialogue corpora, for the first time, offers an opportunity to empirically test theoretical accounts of the expression and inference of rejection in dialogue. In this paper, we test whether topic-independent features motivated by theoretical predictions can be used to recognize rejection in online forums in a topic independent way. Our results show that our theoretically motivated features achieve 66% accuracy, an improvement over a unigram baseline of an absolute 6%.

Connected through hyperedge HE-1 (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"
    - "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"
    - "using summarization to discover argument facets in online idealogical dialog"
    - "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-2 (induced by source paper "recognising agreement and disagreement between stances with reason comparing networks"):
  with members:
    - "attention is all you need"
    - "bilateral multi perspective matching for natural language sentences"
    - "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
    - "long short term memory networks for machine reading"
    - "a decomposable attention model for natural language inference"
    - "stance detection with bidirectional conditional encoding"
    - "stance and sentiment in tweets"
    - "context dependent translation selection using convolutional neural network"

Connected through hyperedge HE-3 (induced by source paper "how did the discussion go discourse act classification in social media conversations"):
  with members:
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "metrics for community analysis a survey"
    - "distributed representations of sentences and documents"
    - "sequence to sequence learning with neural networks"
    - "a convolutional neural network for modelling sentences"
    - "distributed representations of words and phrases and their compositionality"
    - "recurrent convolutional neural networks for discourse compositionality"
    - "user level sentiment analysis incorporating social networks"

Connected through hyperedge HE-4 (induced by source paper "agree to disagree improving disagreement detection with dual grus"):
  with members:
    - "word affect intensities"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "bilateral multi perspective matching for natural language sentences"
    - "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"
    - "stance and sentiment in tweets"
    - "abcnn attention based convolutional neural network for modeling sentence pairs"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a large annotated corpus for learning natural language inference"

Connected through hyperedge HE-5 (induced by source paper "debbie the debate bot of the future"):
  with members:
    - "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"
    - "using summarization to discover argument facets in online idealogical dialog"

Connected through hyperedge HE-6 (induced by source paper "nlds ucsc at semeval 2016 task 6 a semi supervised approach to detecting stance in tweets"):
  with members:
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a new anew evaluation of a word list for sentiment analysis in microblogs"
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-7 (induced by source paper "improving agreement and disagreement identification in online discussions with a socially tuned sentiment lexicon"):
  with members:
    - "get out the vote determining support or opposition from congressional floor debate transcripts"

Connected through hyperedge HE-8 (induced by source paper "using summarization to discover argument facets in online idealogical dialog"):
  with members:
    - "scikit learn machine learning in python"

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)