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: "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
  Abstract: More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic Natural Language Processing resources such as news, highly social dialogue is frequent in social media, making it a challenging context for NLP. This paper tests a bootstrapping method, originally proposed in a monologic domain, to train classifiers to identify two different types of subjective language in dialogue: sarcasm and nastiness. We explore two methods of developing linguistic indicators to be used in a first level classifier aimed at maximizing precision at the expense of recall. The best performing classifier for the first phase achieves 54% precision and 38% recall for sarcastic utterances. We then use general syntactic patterns from previous work to create more general sarcasm indicators, improving precision to 62% and recall to 52%. To further test the generality of the method, we then apply it to bootstrapping a classifier for nastiness dialogic acts. Our first phase, using crowdsourced nasty indicators, achieves 58% precision and 49% recall, which increases to 75% precision and 62% recall when we bootstrap over the first level with generalized syntactic patterns.

Connected through hyperedge HE-1 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection"):
  with members:
    - "nihrio at semeval 2018 task 3 a simple and accurate neural network model for irony detection in twitter"
    - "ntua slp at semeval 2018 task 3 tracking ironic tweets using ensembles of word and character level attentive rnns"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "are word embedding based features useful for sarcasm detection"
    - "xgboost a scalable tree boosting system"

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"
    - "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-3 (induced by source paper "reasoning with sarcasm by reading in between"):
  with members:
    - "attention is all you need"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "a large self annotated corpus for sarcasm"
    - "compare compress and propagate enhancing neural architectures with alignment factorization for natural language inference"
    - "learning to attend via word aspect associative fusion for aspect based sentiment analysis"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-4 (induced by source paper "survey of the state of the art in natural language generation core tasks applications and evaluation"):
  with members:
    - "toward controlled generation of text"
    - "controlling linguistic style aspects in neural language generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "affective neural response generation"
    - "a persona based neural conversation model"
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "query focused opinion summarization for user generated content"
    - "spice semantic propositional image caption evaluation"

Connected through hyperedge HE-5 (induced by source paper "deep learning for user comment moderation"):
  with members:
    - "hunting for troll comments in news community forums"
    - "lstm a search space odyssey"
    - "deep learning for hate speech detection in tweets"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "ex machina personal attacks seen at scale"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "antisocial behavior in online discussion communities"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-6 (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"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "measuring the similarity of sentential arguments in dialogue"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "a large self annotated corpus for sarcasm"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "automatic sarcasm detection a survey"
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-8 (induced by source paper "creating and characterizing a diverse corpus of sarcasm in dialogue"):
  with members:
    - "getting reliable annotations for sarcasm in online dialogues"
    - "visualizing and understanding neural models in nlp"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
    - "distributed representations of words and phrases and their compositionality"

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)