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 "deep two path semi supervised learning for fake news detection"):
  Abstract: News in social media such as Twitter has been generated in high volume and speed. However, very few of them can be labeled (as fake or true news) in a short time. In order to achieve timely detection of fake news in social media, a novel deep two-path semi-supervised learning model is proposed, where one path is for supervised learning and the other is for unsupervised learning. These two paths implemented with convolutional neural networks are jointly optimized to enhance detection performance. In addition, we build a shared convolutional neural networks between these two paths to share the low level features. Experimental results using Twitter datasets show that the proposed model can recognize fake news effectively with very few labeled data.

Includes members:
  Member "all in one multi task learning for rumour verification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "a survey on natural language processing for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "liar liar pants on fire a new benchmark dataset for fake news detection":
    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 "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "temporal ensembling for semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "learning reporting dynamics during breaking news for rumour detection in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rumour detection via news propagation dynamics and user representation learning")
  Member "analysing how people orient to and spread rumours in social media by looking at conversational threads":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "two path deep semi supervised learning for timely fake news detection")
  Member "distributed representations of words and phrases and their compositionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")

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)