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 "representing social media users for sarcasm detection"):
  Abstract: We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.

Includes members:
  Member "reasoning with sarcasm by reading in between":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exploring author context for detecting intended vs perceived sarcasm")
  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 "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity")
  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 "a large self annotated corpus for sarcasm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "read beyond the lines understanding the implied textual meaning via a skim and intensive reading model")
  Member "advances in pre training distributed word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "reporting score distributions makes a difference performance study of lstm networks for sequence tagging":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning")
  Member "a deeper look into sarcastic tweets using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification")
  Member "modelling context with user embeddings for sarcasm detection in social media":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a transformer based approach to irony and sarcasm detection")

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)