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 "danish stance classification and rumour resolution"):
  Abstract: The Internet is rife with flourishing rumours that spread through microblogs and social media. Recent work has shown that analysing the stance of the crowd towards a rumour is a good indicator for its veracity. One state-of-the-art system uses an LSTM neural network to automatically classify stance for posts on Twitter by considering the context of a whole branch, while another, more simple Decision Tree classifier, performs at least as well by performing careful feature engineering. One approach to predict the veracity of a rumour is to use stance as the only feature for a Hidden Markov Model (HMM). This thesis generates a stance-annotated Reddit dataset for the Danish language, and implements various models for stance classification. Out of these, a Linear Support Vector Machine provides the best results with an accuracy of 0.76 and macro F1 score of 0.42. Furthermore, experiments show that stance labels can be used across languages and platforms with a HMM to predict the veracity of rumours, achieving an accuracy of 0.82 and F1 score of 0.67. Even higher scores are achieved by relying only on the Danish dataset. In this case veracity prediction scores an accuracy of 0.83 and an F1 of 0.68. Finally, when using automatic stance labels for the HMM, only a small drop in performance is observed, showing that the implemented system can have practical applications.

Includes members:
  Member "fake news detection using stance classification a survey":
    also connected through other hyperedges:
      (none)
  Member "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers":
    also connected through other hyperedges:
      (none)
  Member "clearumor at semeval 2019 task 7 convolving elmo against rumors":
    also connected through other hyperedges:
      (none)
  Member "a retrospective analysis of the fake news challenge stance detection task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "the fact extraction and verification fever shared task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions")
  Member "detection and resolution of rumours in social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "learning word vectors for 157 languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "rumoureval 2019 determining rumour veracity and support for rumours":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "back to the future sequential alignment of text representations")

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)