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 "maximizing clearance rate by penalizing redundant task assignment in mobile crowdsensing auctions"):
  Abstract: This research is concerned with the effectiveness of auctions-based task assignment and management in centralized, participatory Mobile Crowdsensing (MCS) systems. During auctions, sensing tasks are matched with participants based on bids and incentives that are provided by the participants and the platform respectively. Recent literature addressed several challenges in auctions including untruthful bidding and malicious participants. Our recent work started addressing another challenge, namely, the maximization of clearance rate (CR) in sensing campaigns, i.e., the percentage of the accomplished sensing tasks. In this research, we propose a new objective function for matching tasks with participants, in order to achieve CR-maximized, reputation-aware auctions. Particularly, we penalize redundant task assignment, where a task is assigned to multiple participants, which can consume the budget unnecessarily. We observe that the less the bidders on a certain task, the higher the priority it should be assigned, to get accomplished. Hence, we introduce a new factor, the task redundancy factor in managing auctions. Through extensive simulations under varying conditions of sensing campaigns, and given a fixed budget, we show that penalizing redundancy (giving higher priority to unpopular tasks) yields significant CR increases of approximately 50%, compared to the highest clearance rates in the recent literature.

Includes members:
  Member "maximizing clearance rate of reputation aware auctions in mobile crowdsensing":
    also connected through other hyperedges:
      (none)
  Member "distributed time sensitive task selection in mobile crowdsensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trajectory design for multiple uav assisted wireless networks")
  Member "efficient opportunistic sensing using mobile collaborative platform mosden":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spatial temporal correlation approach for data reduction in cluster based sensor 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)