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 "detection and resolution of rumours in social media a survey"):
  Abstract: Despite the increasing use of social media platforms for information and news gathering, its unmoderated nature often leads to the emergence and spread of rumours, i.e., items of information that are unverified at the time of posting. At the same time, the openness of social media platforms provides opportunities to study how users share and discuss rumours, and to explore how to automatically assess their veracity, using natural language processing and data mining techniques. In this article, we introduce and discuss two types of rumours that circulate on social media: long-standing rumours that circulate for long periods of time, and newly emerging rumours spawned during fast-paced events such as breaking news, where reports are released piecemeal and often with an unverified status in their early stages. We provide an overview of research into social media rumours with the ultimate goal of developing a rumour classification system that consists of four components: rumour detection, rumour tracking, rumour stance classification, and rumour veracity classification. We delve into the approaches presented in the scientific literature for the development of each of these four components. We summarise the efforts and achievements so far toward the development of rumour classification systems and conclude with suggestions for avenues for future research in social media mining for the detection and resolution of rumours.

Includes members:
  Member "rumor detection and classification for twitter data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a semi supervised approach to message stance classification")
  Member "supporting the use of user generated content in journalistic practice":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the rumour mill making the spread of misinformation explicit and tangible")
  Member "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "simple open stance classification for rumour analysis":
    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 "an efficient randomized algorithm for rumor blocking in online social networks":
    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 "turing at semeval 2017 task 8 sequential approach to rumour stance classification with branch lstm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a semi supervised approach to message stance classification")
  Member "determining the veracity of rumours on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a semi supervised approach to message stance classification")
  Member "hoaxy a platform for tracking online misinformation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "topology comparison of twitter diffusion networks effectively reveals misleading information")

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)