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: "indonesian social media sentiment analysis with sarcasm detection"
  Abstract: Sarcasm is considered one of the most difficult problem in sentiment analysis. In our observation on Indonesian social media, for certain topics, people tend to criticize something using sarcasm. Here, we proposed two additional features to detect sarcasm after a common sentiment analysis is conducted. The features are the negativity information and the number of interjection words. We also employed translated SentiWordNet in the sentiment classification. All the classifications were conducted with machine learning algorithms. The experimental results showed that the additional features are quite effective in the sarcasm detection.

Connected through hyperedge HE-1 (induced by source paper "facebook reaction based emotion classifier as cue for sarcasm detection"):
  with members:
    - "anger is more influential than joy sentiment correlation in weibo"

Connected through hyperedge HE-2 (induced by source paper "automatic sarcasm detection a survey"):
  with members:
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "a large self annotated corpus for sarcasm"
    - "harnessing cognitive features for sarcasm detection"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"
    - "are word embedding based features useful for sarcasm detection"
    - "modelling context with user embeddings for sarcasm detection in social media"
    - "who would have thought of that a hierarchical topic model for extraction of sarcasm prevalent topics and sarcasm detection"

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)