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: "getting reliable annotations for sarcasm in online dialogues"
  Abstract: The language used in online forums differs in many ways from that of traditional language resources such as news. One difference is the use and frequency of nonliteral, subjective dialogue acts such as sarcasm. Whether the aim is to develop a theory of sarcasm in dialogue, or engineer automatic methods for reliably detecting sarcasm, a major challenge is simply the difficulty of getting enough reliably labelled examples. In this paper we describe our work on methods for achieving highly reliable sarcasm annotations from untrained annotators on Mechanical Turk. We explore the use of a number of common statistical reliability measures, such as Kappa, Karger's, Majority Class, and EM. We show that more sophisticated measures do not appear to yield better results for our data than simple measures such as assuming that the correct label is the one that a majority of Turkers apply.

Connected through hyperedge HE-1 (induced by source paper "argument strength is in the eye of the beholder audience effects in persuasion"):
  with members:
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "learning fine grained knowledge about contingent relations between everyday events"
    - "measuring the similarity of sentential arguments in dialogue"
    - "learning latent local conversation modes for predicting community endorsement in online discussions"
    - "winning arguments interaction dynamics and persuasion strategies in good faith online discussions"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"

Connected through hyperedge HE-2 (induced by source paper "a large self annotated corpus for sarcasm"):
  with members:
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "automatic sarcasm detection a survey"
    - "inferring networks of substitutable and complementary products"

Connected through hyperedge HE-3 (induced by source paper "are you serious rhetorical questions and sarcasm in social media dialog"):
  with members:
    - "creating and characterizing a diverse corpus of sarcasm in dialogue"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-4 (induced by source paper "creating and characterizing a diverse corpus of sarcasm in dialogue"):
  with members:
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "visualizing and understanding neural models in nlp"
    - "and that s a fact distinguishing factual and emotional argumentation in online dialogue"
    - "distributed representations of words and phrases and their compositionality"

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)