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 semi supervised approach to message stance classification"):
  Abstract: Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages’ stance towards the rumour, a feature known as the “wisdom of the crowd.” Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper, we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-the-art to demonstrate our algorithms’ performance regarding accuracy, speed, and scalability for real-time applications.

Includes members:
  Member "rumor detection and classification for twitter data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "quickstop a markov optimal stopping approach for quickest misinformation detection")
  Member "detection and resolution of rumours in social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity")
  Member "determining the veracity of rumours on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards understanding the information ecosystem through the lens of multiple web communities")
  Member "using gaussian processes for rumour stance classification in social media":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discourse aware rumour stance classification in social media using sequential classifiers")
  Member "a tutorial on online supervised learning with applications to node classification in social networks":
    also connected through other hyperedges:
      (none)
  Member "stance classification in rumours as a sequential task exploiting the tree structure of social media conversations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity")

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.SI(Social and Information Networks)