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 "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity"):
  Abstract: Human communication often involves the use of verbal irony or sarcasm, where the speakers usually mean the opposite of what they say. To better understand how verbal irony is expressed by the speaker and interpreted by the hearer we conduct a crowdsourcing task: given an utterance expressing verbal irony, users are asked to verbalize their interpretation of the speaker's ironic message. We propose a typology of linguistic strategies for verbal irony interpretation and link it to various theoretical linguistic frameworks. We design computational models to capture these strategies and present empirical studies aimed to answer three questions: (1) what is the distribution of linguistic strategies used by hearers to interpret ironic messages?; (2) do hearers adopt similar strategies for interpreting the speaker's ironic intent?; and (3) does the type of semantic incongruity in the ironic message (explicit vs. implicit) influence the choice of interpretation strategies by the hearers?

Includes members:
  Member "exploring author context for detecting intended vs perceived sarcasm":
    also connected through other hyperedges:
      (none)
  Member "towards multimodal sarcasm detection an _obviously_ perfect paper":
    also connected through other hyperedges:
      (none)
  Member "sarcasm analysis using conversation context":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "empirical analysis of multi task learning for reducing model bias in toxic comment detection")
  Member "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sarcasm analysis using conversation context")
  Member "cascade contextual sarcasm detection in online discussion forums":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a transformer based approach to irony and sarcasm detection")
  Member "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation")
  Member "harnessing cognitive features for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper")
  Member "the role of conversation context for sarcasm detection in online interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sarcasm analysis using conversation context")

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)