human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a trust based recruitment framework for multi hop social participatory sensing"
  Abstract: The idea of social participatory sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social participatory sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.

Connected through hyperedge HE-1 (induced by source paper "quality of information in mobile crowdsensing survey and research challenges"):
  with members:
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "taskme multi task allocation in mobile crowd sensing"
    - "sacrm social aware crowdsourcing with reputation management in mobile sensing"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "a survey on truth discovery"
    - "mining users significant driving routes with low power sensors"
    - "ear phone a context aware noise mapping using smart phones"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-2 (induced by source paper "big data analytics using cloud and crowd"):
  with members:
    - "an iterative method for calculating robust rating scores"
    - "crowder crowdsourcing entity resolution"
    - "serf and turf crowdturfing for fun and profit"
    - "human powered sorts and joins"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-3 (induced by source paper "sacrm social aware crowdsourcing with reputation management in mobile sensing"):
  with members:
    - "fairness and social welfare in incentivizing participatory sensing"
    - "reputation based incentive protocols in crowdsourcing applications"

Question: Please tell me which class the center node 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)