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 model to measure the spread power of rumors"):
  Abstract: Nowadays, a significant portion of daily interacted posts in social media are infected by rumors. This study investigates the problem of rumor analysis in different areas from other researches. It tackles the unaddressed problem related to calculating the Spread Power of Rumor (SPR) for the first time and seeks to examine the spread power as the function of multi-contextual features. For this purpose, the theory of Allport and Postman will be adopted. In which it claims that there are two key factors determinant to the spread power of rumors, namely importance and ambiguity. The proposed Rumor Spread Power Measurement Model (RSPMM) computes SPR by utilizing a textual-based approach, which entails contextual features to compute the spread power of the rumors in two categories: False Rumor (FR) and True Rumor (TR). Totally 51 contextual features are introduced to measure SPR and their impact on classification are investigated, then 42 features in two categories "importance" (28 features) and "ambiguity" (14 features) are selected to compute SPR. The proposed RSPMM is verified on two labelled datasets, which are collected from Twitter and Telegram. The results show that (i) the proposed new features are effective and efficient to discriminate between FRs and TRs. (ii) the proposed RSPMM approach focused only on contextual features while existing techniques are based on Structure and Content features, but RSPMM achieves considerably outstanding results (F-measure=83%). (iii) The result of T-Test shows that SPR criteria can significantly distinguish between FR and TR, besides it can be useful as a new method to verify the trueness of rumors.

Includes members:
  Member "a speech act classifier for persian texts and its application in identify speech act of rumors":
    also connected through other hyperedges:
      (none)
  Member "a machine learning approach to persian text readability assessment using a crowdsourced dataset":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "text as environment a deep reinforcement learning text readability assessment model")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "text classification with lexicon from preattention mechanism")

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.AI(Artificial Intelligence)