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: "sacrm social aware crowdsourcing with reputation management in mobile sensing"
  Abstract: Abstract   Mobile sensing has become a promising paradigm for mobile users to obtain information by task crowdsourcing. However, due to the social preferences of mobile users, the quality of sensing reports may be impacted by the underlying social attributes and selfishness of individuals. Therefore, it is crucial to consider the social impacts and trustworthiness of mobile users when selecting task participants in mobile sensing. In this paper, we propose a  S ocial  A ware  C rowdsourcing with  R eputation  M anagement (SACRM) scheme to select the well-suited participants and allocate the task rewards in mobile sensing. Specifically, we consider the social attributes, task delay and reputation in crowdsourcing and propose a participant selection scheme to choose the well-suited participants for the sensing task under a fixed task budget. A report assessment and rewarding scheme is also introduced to measure the quality of the sensing reports and allocate the task rewards based the assessed report quality. In addition, we develop a reputation management scheme to evaluate the trustworthiness and cost performance ratio of mobile users for participant selection. Theoretical analysis and extensive simulations demonstrate that SACRM can efficiently improve the crowdsourcing utility and effectively stimulate the participants to improve the quality of their sensing reports.

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"
    - "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 trust based recruitment framework for multi hop social participatory sensing"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-2 (induced by source paper "incentive mechanisms for participatory sensing survey and research challenges"):
  with members:
    - "providing long term participation incentive in participatory sensing"
    - "optimal prizes for all pay contests in heterogeneous crowdsourcing"
    - "the importance of being earnest in crowdsourcing systems"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "mining users significant driving routes with low power sensors"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "truthful scheduling mechanisms for powering mobile crowdsensing"
    - "mechanism design for time critical and cost critical task execution via crowdsourcing"

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.NI(Networking and Internet Architecture)