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 "effectiveness of data driven induction of semantic spaces and traditional classifiers for sarcasm detection"):
  Abstract: Irony and sarcasm are two complex linguistic phenomena that are widely used in everyday language and especially over the social media, but they represent two serious issues for automated text understanding. Many labelled corpora have been extracted from several sources to accomplish this task, and it seems that sarcasm is conveyed in different ways for different domains. Nonetheless, very little work has been done for comparing different methods among the available corpora. Furthermore, usually, each author collects and uses its own dataset to evaluate his own method. In this paper, we show that sarcasm detection can be tackled by applying classical machine learning algorithms to input texts sub-symbolically represented in a Latent Semantic space. The main consequence is that our studies establish both reference datasets and baselines for the sarcasm detection problem that could serve to the scientific community to test newly proposed methods.

Includes members:
  Member "nihrio at semeval 2018 task 3 a simple and accurate neural network model for irony detection in twitter":
    also connected through other hyperedges:
      (none)
  Member "ntua slp at semeval 2018 task 3 tracking ironic tweets using ensembles of word and character level attentive rnns":
    also connected through other hyperedges:
      (none)
  Member "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "xslue a benchmark and analysis platform for cross style language understanding and evaluation")
  Member "the interpretation of dream meaning resolving ambiguity using latent semantic analysis in a small corpus of text":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "advances in machine learning for the behavioral sciences")
  Member "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating causal effects of tone in online debates")
  Member "creating and characterizing a diverse corpus of sarcasm in dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attention based conditioning methods for external knowledge integration")
  Member "a deeper look into sarcastic tweets using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling sentiment dependencies with graph convolutional networks for aspect level sentiment classification")
  Member "are word embedding based features useful for sarcasm detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humor in word embeddings cockamamie gobbledegook for nincompoops")

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)