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 "news labeling as early as possible real or fake"):
  Abstract: Making disguise between real and fake news propagation through online social networks is an important issue in many applications. The time gap between the news release time and detection of its label is a significant step towards broadcasting the real information and avoiding the fake. Therefore, one of the challenging tasks in this area is to identify fake and real news in early stages of propagation. However, there is a trade-off between minimizing the time gap and maximizing accuracy. Despite recent efforts in detection of fake news, there has been no significant work that explicitly incorporates early detection in its model. In this paper, we focus on accurate early labeling of news, and propose a model by considering earliness both in modeling and prediction. The proposed method utilizes recurrent neural networks with a novel loss function, and a new stopping rule. Given the context of news, we first embed it with a class-specific text representation. Then, we utilize the available public profile of users, and speed of news diffusion, for early labeling of the news. Experiments on real datasets demonstrate the effectiveness of our model both in terms of early labelling and accuracy, compared to the state of the art baseline and models.

Includes members:
  Member "studying fake news via network analysis detection and mitigation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "fake news a survey of research detection methods and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "learning class specific word representations for early detection of hoaxes in social media":
    also connected through other hyperedges:
      (none)
  Member "fakedetector effective fake news detection with deep diffusive neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hgat hierarchical graph attention network for fake news detection")
  Member "detection and resolution of rumours in social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "automatic rumor detection on microblogs a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mining disinformation and fake news concepts methods and recent advancements")
  Member "beyond news contents the role of social context for fake news detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attributed multi relational attention network for fact checking url recommendation")
  Member "fake news detection on social media a data mining perspective":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")

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)