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: "providing long term participation incentive in participatory sensing"
  Abstract: Providing an adequate long-term user participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under asymmetry of current information. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% ∼ 90%) and social performance (e.g., increasing the social welfare by 15% ∼ 80%).

Connected through hyperedge HE-1 (induced by source paper "small profits and quick returns an incentive mechanism design for iot based crowdsourcing under continuous platform competition"):
  with members:
    - "crowdsourcing with tullock contests a new perspective"

Connected through hyperedge HE-2 (induced by source paper "a budget feasible peer graded mechanism for iot based crowdsourcing"):
  with members:
    - "quality control in crowdsourcing a survey of quality attributes assessment techniques and assurance actions"
    - "a budget feasible mechanism for hiring doctors in e healthcare"
    - "incentives for truthful peer grading"
    - "online assignment of heterogeneous tasks in crowdsourcing markets"
    - "on cost effective incentive mechanisms in microtask crowdsourcing"
    - "budget feasible mechanisms"

Connected through hyperedge HE-3 (induced by source paper "performance bound analysis for crowdsourced mobile video streaming"):
  with members:
    - "a game theoretic analysis of user behaviors in crowdsourced wireless community networks"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "nova qoe driven optimization of dash based video delivery in networks"

Connected through hyperedge HE-4 (induced by source paper "ps sim a framework for scalable simulation of participatory sensing data"):
  with members:
    - "incentive mechanisms for participatory sensing survey and research challenges"

Connected through hyperedge HE-5 (induced by source paper "generalized lottery trees budget consistent incentive tree mechanisms for crowdsourcing"):
  with members:
    - "time critical social mobilization"

Connected through hyperedge HE-6 (induced by source paper "scalable mobile crowdsensing via peer to peer data sharing"):
  with members:
    - "economic analysis of crowdsourced wireless community networks"
    - "dynamic routing for social information sharing"
    - "an integrated spectrum and information market for green cognitive communications"
    - "a game theoretic analysis of user behaviors in crowdsourced wireless community networks"
    - "mine gold to deliver green cognitive communications"
    - "hysim a hybrid spectrum and information market for tv white space networks"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"

Connected through hyperedge HE-7 (induced by source paper "data centric mobile crowdsensing"):
  with members:
    - "scalable mobile crowdsensing via peer to peer data sharing"
    - "two sided matching based cooperative spectrum sharing"
    - "economic analysis of crowdsourced wireless community networks"
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "dynamic routing for social information sharing"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "combining spot and futures markets a hybrid market approach to dynamic spectrum access"
    - "motivating smartphone collaboration in data acquisition and distributed computing"

Connected through hyperedge HE-8 (induced by source paper "theseus incentivizing truth discovery in mobile crowd sensing systems"):
  with members:
    - "centurion incentivizing multi requester mobile crowd sensing"
    - "the value of privacy strategic data subjects incentive mechanisms and fundamental limits"
    - "crowdsourcing with tullock contests a new perspective"
    - "truthful linear regression"
    - "distributed time sensitive task selection in mobile crowdsensing"
    - "linear regression as a non cooperative game"

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.GT(Computer Science and Game Theory)