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 "data centric mobile crowdsensing"):
  Abstract: Mobile crowdsensing (MCS) is a promising sensing paradigm that leverages the diverse embedded sensors in massive mobile devices. A key objective in MCS is to efficiently schedule mobile users to perform multiple sensing tasks. Prior work mainly focused on interactions between the task-layer and the user-layer, without considering tasks' similar data requirements and users' heterogeneous sensing capabilities. In this work, we propose a three-layer data-centric MCS model by introducing a new data-layer between tasks and users, enable different tasks to reuse the same data items, hence effectively leverage both task similarities and user heterogeneities. We formulate a joint task selection and user scheduling problem based on the new framework, aiming at maximizing social welfare. We first analyze the centralized optimization problem with the statistical information of tasks and users, and show the bound of the social welfare gain due to data reuse. Then we consider the two-sided information asymmetry of selfish task-owners and users, and propose a decentralized market mechanism for achieving the centralized social optimality. In particular, considering the NP-hardness of the optimization, we propose a truthful two-sided randomized auction mechanism that ensures computational efficiency and a close-to-optimal performance. Simulations verify the effectiveness of our proposed model and mechanism.

Includes members:
  Member "scalable mobile crowdsensing via peer to peer data sharing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal pricing of internet of things a machine learning approach")
  Member "two sided matching based cooperative spectrum sharing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context aware group buying in ultra dense small cell networks unity is strength")
  Member "economic analysis of crowdsourced wireless community networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi cap optimization for wireless data plans with time flexibility")
  Member "centurion incentivizing multi requester mobile crowd sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multimedia crowdsourcing with bounded rationality a cognitive hierarchy perspective")
  Member "dynamic routing for social information sharing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crowd mecs a novel crowdsourcing framework for mobile edge caching and sharing")
  Member "providing long term participation incentive in participatory sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "small profits and quick returns an incentive mechanism design for iot based crowdsourcing under continuous platform competition")
  Member "distributed time sensitive task selection in mobile crowdsensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trajectory design for multiple uav assisted wireless networks")
  Member "combining spot and futures markets a hybrid market approach to dynamic spectrum access":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scalable mobile crowdsensing via peer to peer data sharing")

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.GT(Computer Science and Game Theory)