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: "representing social media users for sarcasm detection"
  Abstract: We explore two methods for representing authors in the context of textual sarcasm detection: a Bayesian approach that directly represents authors' propensities to be sarcastic, and a dense embedding approach that can learn interactions between the author and the text. Using the SARC dataset of Reddit comments, we show that augmenting a bidirectional RNN with these representations improves performance; the Bayesian approach suffices in homogeneous contexts, whereas the added power of the dense embeddings proves valuable in more diverse ones.

Connected through hyperedge HE-1 (induced by source paper "you shall know a user by the company it keeps dynamic representations for social media users in nlp"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "abusive language detection with graph convolutional networks"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "assessing state of the art sentiment models on state of the art sentiment datasets"
    - "attention is all you need"
    - "a decomposable attention model for natural language inference"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-2 (induced by source paper "towards multimodal sarcasm detection an _obviously_ perfect paper"):
  with members:
    - "emotion recognition in conversation research challenges datasets and recent advances"
    - "meld a multimodal multi party dataset for emotion recognition in conversations"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "cascade contextual sarcasm detection in online discussion forums"
    - "tensor fusion network for multimodal sentiment analysis"
    - "harnessing cognitive features for 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)