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: "creating and characterizing a diverse corpus of sarcasm in dialogue"
  Abstract: The use of irony and sarcasm in social media allows us to study them at scale for the first time. However, their diversity has made it difficult to construct a high-quality corpus of sarcasm in dialogue. Here, we describe the process of creating a large- scale, highly-diverse corpus of online debate forums dialogue, and our novel methods for operationalizing classes of sarcasm in the form of rhetorical questions and hyperbole. We show that we can use lexico-syntactic cues to reliably retrieve sarcastic utterances with high accuracy. To demonstrate the properties and quality of our corpus, we conduct supervised learning experiments with simple features, and show that we achieve both higher precision and F than previous work on sarcasm in debate forums dialogue. We apply a weakly-supervised linguistic pattern learner and qualitatively analyze the linguistic differences in each class.

Connected through hyperedge HE-1 (induced by source paper "attention based conditioning methods for external knowledge integration"):
  with members:
    - "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences"
    - "gating mechanisms for combining character and word level word representations an empirical study"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "robust lexical features for improved neural network named entity recognition"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "reside improving distantly supervised neural relation extraction using side information"

Connected through hyperedge HE-2 (induced by source paper "an embarrassingly simple approach for transfer learning from pretrained language models"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "ntua slp at iest 2018 ensemble of neural transfer methods for implicit emotion classification"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spell once summon anywhere a two level open vocabulary language model"
    - "universal language model fine tuning for text classification"

Connected through hyperedge HE-3 (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"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online 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-4 (induced by source paper "sarcasm analysis using conversation context"):
  with members:
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "conversation modeling on reddit using a graph structured lstm"
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "a large self annotated corpus for sarcasm"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-5 (induced by source paper "detecting offensive content in open domain conversations using two stage semi supervision"):
  with members:
    - "conversational ai the science behind the alexa prize"
    - "automated hate speech detection and the problem of offensive language"
    - "impact of content features for automatic online abuse detection"
    - "deep learning for hate speech detection in tweets"
    - "one step and two step classification for abusive language detection on twitter"
    - "measuring the reliability of hate speech annotations the case of the european refugee crisis"

Connected through hyperedge HE-6 (induced by source paper "deep contextualized word representations for detecting sarcasm and irony"):
  with members:
    - "deep contextualized word representations"
    - "a large self annotated corpus for sarcasm"
    - "automatic sarcasm detection a survey"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-7 (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"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online 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-8 (induced by source paper "data driven dialogue systems for social agents"):
  with members:
    - "automating direct speech variations in stories and games"
    - "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"
    - "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)