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: "automatic sarcasm detection a survey"
  Abstract: Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text. In this paper, we describe datasets, approaches, trends and issues in sarcasm detection. We also discuss representative performance values, shared tasks and pointers to future work, as given in prior works. In terms of resources that could be useful for understanding state-of-the-art, the survey presents several useful illustrations - most prominently, a table that summarizes past papers along different dimensions such as features, annotation techniques, data forms, etc.

Connected through hyperedge HE-1 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation"):
  with members:
    - "deep contextualized word representations for detecting sarcasm and irony"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "detecting and explaining causes from text for a time series event"
    - "automated hate speech detection and the problem of offensive language"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sarcasm detection using hybrid neural network"):
  with members:
    - "comparative study of cnn and rnn for natural language processing"
    - "augmenting end to end dialog systems with commonsense knowledge"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "neural machine translation by jointly learning to align and translate"

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"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "are word embedding based features useful for sarcasm detection"

Connected through hyperedge HE-5 (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"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "are word embedding based features useful for sarcasm detection"

Connected through hyperedge HE-6 (induced by source paper "getting the subtext without the text scalable multimodal sentiment classification from visual and acoustic modalities"):
  with members:
    - "multi attention recurrent network for human communication comprehension"
    - "memory fusion network for multi view sequential learning"
    - "conversational ai the science behind the alexa prize"
    - "multimodal sentiment analysis addressing key issues and setting up baselines"
    - "tensor fusion network for multimodal sentiment analysis"
    - "adversarial examples for evaluating reading comprehension systems"
    - "comparing human and machine errors in conversational speech transcription"
    - "end to end concept word detection for video captioning retrieval and question answering"

Connected through hyperedge HE-7 (induced by source paper "cascade contextual sarcasm detection in online discussion forums"):
  with members:
    - "a large self annotated corpus for sarcasm"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "enriching word vectors with subword information"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "adam a method for stochastic optimization"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "ntua slp at semeval 2018 task 3 tracking ironic tweets using ensembles of word and character level attentive rnns"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "are word embedding based features useful for sarcasm detection"
    - "controlled experiments for word embeddings"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "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)