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 "mapping dis information flow about the mh17 plane crash"):
  Abstract: Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators.

Includes members:
  Member "universal dependency parsing from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning spatiotemporal features via video and text pair discrimination")
  Member "issue framing in online discussion fora":
    also connected through other hyperedges:
      (none)
  Member "loss in translation learning bilingual word mapping with a retrieval criterion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies")
  Member "junk news on military affairs and national security social media disinformation campaigns against us military personnel and veterans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polarization partisanship and junk news consumption over social media in the us")
  Member "framing and agenda setting in russian news a computational analysis of intricate political strategies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "frameaxis characterizing framing bias and intensity with word embedding")
  Member "discourse aware rumour stance classification in social media using sequential classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multifc a real world multi domain dataset for evidence based fact checking of claims")
  Member "neural discourse structure for text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "tweet2vec character based distributed representations for social media":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses")

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)