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 "trust evaluation mechanism for user recruitment in mobile crowd sensing in the internet of things"):
  Abstract: Mobile Crowd-Sensing (MCS) has appeared as a prospective solution for large-scale data collection, leveraging built-in sensors and social applications in mobile devices that enables a variety of Internet of Things (IoT) services. However, the human involvement in MCS results in a high possibility for unintentionally contributing corrupted and falsified data or intentionally spreading disinformation for malevolent purposes, consequently undermining IoT services. Therefore, recruiting trustworthy contributors plays a crucial role in collecting high-quality data and providing better quality of services while minimizing the vulnerabilities and risks to MCS systems. In this article, a novel trust model called Experience-Reputation (E-R) is proposed for evaluating trust relationships between any two mobile device users in a MCS platform. To enable the E-R model, virtual interactions among the users are manipulated by considering an assessment of the quality of contributed data from such users. Based on these interactions, two indicators of trust called Experience and Reputation are calculated accordingly. By incorporating the Experience and Reputation trust indicators (TIs), trust relationships between the users are established, evaluated and maintained. Based on these trust relationships, a novel trust-based recruitment scheme is carried out for selecting the most trustworthy MCS users to contribute to data sensing tasks. In order to evaluate the performance and effectiveness of the proposed trust-based mechanism as well as the E-R trust model, we deploy several recruitment schemes in a MCS testbed which consists of both normal and malicious users. The results highlight the strength of the trust-based scheme as it delivers better quality for MCS services while being able to detect malicious users.

Includes members:
  Member "social network assisted worker recruitment in mobile crowd sensing":
    also connected through other hyperedges:
      (none)
  Member "quality of information in mobile crowdsensing survey and research challenges":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel methodology for designing policies in mobile crowdsensing systems")
  Member "when crowdsourcing meets mobile sensing a social network perspective":
    also connected through other hyperedges:
      (none)
  Member "mobile crowd sensing and computing when participatory sensing meets participatory social media":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the future of misinformation detection new perspectives and trends")
  Member "from participatory sensing to mobile crowd sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource management in cloud networking using economic analysis and pricing models a survey")
  Member "sensing as a service model for smart cities supported by internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fog computing applications taxonomy and requirements")
  Member "internet of things iot a vision architectural elements and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neuromemristive circuits for edge computing a review")
  Member "pagerank standing on the shoulders of giants":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "wikilinkgraphs a complete longitudinal and multi language dataset of the wikipedia link networks")

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.NI(Networking and Internet Architecture)