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 "automatic rumor detection on microblogs a survey"):
  Abstract: The ever-increasing amount of multimedia content on modern social media platforms are valuable in many applications. While the openness and convenience features of social media also foster many rumors online. Without verification, these rumors would reach thousands of users immediately and cause serious damages. Many efforts have been taken to defeat online rumors automatically by mining the rich content provided on the open network with machine learning techniques. Most rumor detection methods can be categorized in three paradigms: the hand-crafted features based classification approaches, the propagation-based approaches and the neural networks approaches. In this survey, we introduce a formal definition of rumor in comparison with other definitions used in literatures. We summary the studies of automatic rumor detection so far and present details in three paradigms of rumor detection. We also give an introduction on existing datasets for rumor detection which would benefit following researches in this area. We give our suggestions for future rumors detection on microblogs as a conclusion.

Includes members:
  Member "detection and resolution of rumours in social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "detection and analysis of 2016 us presidential election related rumors on twitter":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hgat hierarchical graph attention network for fake news detection")
  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 "on early stage debunking rumors on twitter leveraging the wisdom of weak learners":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the mass fake news and cognition security")
  Member "call attention to rumors deep attention based recurrent neural networks for early rumor detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combating fake news a survey on identification and mitigation techniques")
  Member "csi a hybrid deep model for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  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 "image credibility analysis with effective domain transferred deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discover your social identity from what you tweet a content based approach")

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)