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 "a retrospective analysis of the fake news challenge stance detection task"):
  Abstract: The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance classification task as a crucial first step towards detecting fake news. To date, there is no in-depth analysis paper to critically discuss FNC-1's experimental setup, reproduce the results, and draw conclusions for next-generation stance classification methods. In this paper, we provide such an in-depth analysis for the three top-performing systems. We first find that FNC-1's proposed evaluation metric favors the majority class, which can be easily classified, and thus overestimates the true discriminative power of the methods. Therefore, we propose a new F1-based metric yielding a changed system ranking. Next, we compare the features and architectures used, which leads to a novel feature-rich stacked LSTM model that performs on par with the best systems, but is superior in predicting minority classes. To understand the methods' ability to generalize, we derive a new dataset and perform both in-domain and cross-domain experiments. Our qualitative and quantitative study helps interpreting the original FNC-1 scores and understand which features help improving performance and why. Our new dataset and all source code used during the reproduction study are publicly available for future research.

Includes members:
  Member "a simple but tough to beat baseline for the fake news challenge stance detection task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards quantifying the distance between opinions")
  Member "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a feature driven approach for identifying pathogenic social media accounts")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "parsing argumentation structures in persuasive essays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "stance detection with bidirectional conditional encoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains")
  Member "mitre at semeval 2016 task 6 transfer learning for stance detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dan dual view representation learning for adapting stance classifiers to new domains")
  Member "nrc canada building the state of the art in sentiment analysis of tweets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study")

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.IR(Information Retrieval)