human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation")
  Member "a large self annotated corpus for sarcasm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model")
  Member "harnessing cognitive features for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity")
  Member "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection")
  Member "a deeper look into sarcastic tweets using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification")
  Member "are word embedding based features useful for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection")
  Member "modelling context with user embeddings for sarcasm detection in social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a transformer based approach to irony and sarcasm detection")
  Member "who would have thought of that a hierarchical topic model for extraction of sarcasm prevalent topics and sarcasm detection":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)