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 "sarcasm analysis using conversation context"):
  Abstract: Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, the speaker's sarcastic intent is not always apparent without additional context. Focusing on social media discussions, we investigate three issues: (1) does modeling conversation context help in sarcasm detection; (2) can we identify what part of conversation context triggered the sarcastic reply; and (3) given a sarcastic post that contains multiple sentences, can we identify the specific sentence that is sarcastic. To address the first issue, we investigate several types of Long Short-Term Memory (LSTM) networks that can model both the conversation context and the current turn. We show that LSTM networks with sentence-level attention on context and current turn, as well as the conditional LSTM network (Rocktaschel et al. 2016), outperform the LSTM model that reads only the current turn. As conversation context, we consider the prior turn, the succeeding turn or both. Our computational models are tested on two types of social media platforms: Twitter and discussion forums. We discuss several differences between these datasets ranging from their size to the nature of the gold-label annotations. To address the last two issues, we present a qualitative analysis of attention weights produced by the LSTM models (with attention) and discuss the results compared with human performance on the two tasks.

Includes members:
  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-1 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity")
  Member "conversation modeling on reddit using a graph structured lstm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint effects of context and user history for predicting online conversation re entries")
  Member "are you serious rhetorical questions and sarcasm in social media dialog":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition")
  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 "harnessing cognitive features for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity")
  Member "the role of conversation context for sarcasm detection in online interactions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity")
  Member "creating and characterizing a diverse corpus of sarcasm in dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention based conditioning methods for external knowledge integration")
  Member "are word embedding based features useful for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effectiveness of data driven induction of semantic spaces and traditional classifiers for 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)