human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "interpreting verbal irony linguistic strategies and the connection to thetype of semantic incongruity"):
  with members:
    - "exploring author context for detecting intended vs perceived sarcasm"
    - "towards multimodal sarcasm detection an _obviously_ perfect paper"
    - "sarcasm analysis using conversation context"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-2 (induced by source paper "sarcasm analysis using conversation context"):
  with members:
    - "conversation modeling on reddit using a graph structured lstm"
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "a large self annotated corpus for sarcasm"
    - "harnessing cognitive features for sarcasm detection"
    - "the role of conversation context for sarcasm detection in online interactions"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "are word embedding based features useful for sarcasm detection"
    - "a decomposable attention model for natural language inference"

Connected through hyperedge HE-3 (induced by source paper "representing social media users for sarcasm detection"):
  with members:
    - "reasoning with sarcasm by reading in between"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "a large self annotated corpus for sarcasm"
    - "advances in pre training distributed word representations"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "exploiting social network structure for person to person sentiment analysis"

Question: Please tell me which class the center node 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)