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 "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"):
  Abstract: Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media platforms: $Twitter$ and $Reddit$. Classification and frequency analysis show that for $Twitter$, typographic markers such as emoticons and emojis are the most discriminative markers to recognize ironic utterances, while for $Reddit$ the morphological markers (e.g., interjections, tag questions) are the most discriminative.

Includes members:
  Member "are you serious rhetorical questions and sarcasm in social media dialog":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sarcasm analysis using conversation context")
  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 "the role of conversation context for sarcasm detection in online interactions":
    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 "grasping the finer point a supervised similarity network for metaphor detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning outside the box discourse level features improve metaphor identification")
  Member "automatic sarcasm detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation")

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)