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: "different absorption from the same sharing sifted multi task learning for fake news detection"
  Abstract: Recently, neural networks based on multi-task learning have achieved promising performance on fake news detection, which focus on learning shared features among tasks as complementary features to serve different tasks. However, in most of the existing approaches, the shared features are completely assigned to different tasks without selection, which may lead to some useless and even adverse features integrated into specific tasks. In this paper, we design a sifted multi-task learning method with a selected sharing layer for fake news detection. The selected sharing layer adopts gate mechanism and attention mechanism to filter and select shared feature flows between tasks. Experiments on two public and widely used competition datasets, i.e. RumourEval and PHEME, demonstrate that our proposed method achieves the state-of-the-art performance and boosts the F1-score by more than 0.87%, 1.31%, respectively.

Connected through hyperedge HE-1 (induced by source paper "multi task learning network for emotion recognition in conversation"):
  with members:
    - "multi task networks with universe group and task feature learning"
    - "knowledge enriched transformer for emotion detection in textual conversations"
    - "higru hierarchical gated recurrent units for utterance level emotion recognition"
    - "multi task learning with language modeling for question generation"
    - "deep contextualized word representations"
    - "dialoguernn an attentive rnn for emotion detection in conversations"
    - "emotion detection on tv show transcripts with sequence based convolutional neural networks"
    - "end to end multimodal emotion recognition using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "discovering differential features adversarial learning for information credibility evaluation"):
  with members:
    - "the role of user profile for fake news detection"
    - "multinomial adversarial networks for multi domain text classification"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "fake cures user centric modeling of health misinformation in social media"
    - "deep neural networks for bot detection"
    - "multi task learning by deep collaboration and application in facial landmark detection"
    - "neural networks for information retrieval"
    - "like sheep among wolves characterizing hateful users on twitter"

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)