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: "the role of conversation context for sarcasm detection in online interactions"
  Abstract: Computational models for sarcasm detection have often relied on the content of utterances in isolation. However, speaker's sarcastic intent is not always obvious without additional context. Focusing on social media discussions, we investigate two issues: (1) does modeling of conversation context help in sarcasm detection and (2) can we understand what part of conversation context triggered the sarcastic reply. 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 sarcastic response. We show that the conditional LSTM network (Rocktaschel et al., 2015) and LSTM networks with sentence level attention on context and response outperform the LSTM model that reads only the response. To address the second issue, 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 task.

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"
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "harnessing cognitive features for sarcasm detection"
    - "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:
    - "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"
    - "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"
    - "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 "attentional multi reading sarcasm detection"):
  with members:
    - "event nugget detection with forward backward recurrent neural networks"
    - "dr bilstm dependent reading bidirectional lstm for natural language inference"
    - "a large self annotated corpus for sarcasm"
    - "modelling context with user embeddings for sarcasm detection in social media"

Connected through hyperedge HE-4 (induced by source paper "with 1 follower i must be awesome p exploring the role of irony markers in irony recognition"):
  with members:
    - "are you serious rhetorical questions and sarcasm in social media dialog"
    - "a large self annotated corpus for sarcasm"
    - "grasping the finer point a supervised similarity network for metaphor detection"
    - "automatic sarcasm detection a survey"

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)