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: "incentives for privacy tradeoff in community sensing"
  Abstract: Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications. Yet, reasonable privacy concerns often limit the access to such data streams. How should systems valuate and negotiate access to private information, for example in return for monetary incentives? How should they optimally choose the participants from a large population of strategic users with privacy concerns, and compensate them for information shared? In this paper, we address these questions and present a novel mechanism, SeqTGreedy, for budgeted recruitment of participants in community sensing. We first show that privacy tradeoffs in community sensing can be cast as an adaptive submodular optimization problem. We then design a budget feasible, incentive compatible (truthful) mechanism for adaptive submodular maximization, which achieves near-optimal utility for a large class of sensing applications. This mechanism is general, and of independent interest. We demonstrate the effectiveness of our approach in a case study of air quality monitoring, using data collected from the Mechanical Turk platform. Compared to the state of the art, our approach achieves up to 30% reduction in cost in order to achieve a desired level of utility.

Connected through hyperedge HE-1 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online"):
  with members:
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "on budget feasible mechanism design for symmetric submodular objectives"
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "greed is good near optimal submodular maximization via greedy optimization"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "submodular secretary problems cardinality matching and linear constraints"

Connected through hyperedge HE-2 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities"):
  with members:
    - "prediction based task assignment in spatial crowdsourcing"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "taskme multi task allocation in mobile crowd sensing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-3 (induced by source paper "social network assisted worker recruitment in mobile crowd sensing"):
  with members:
    - "taskme multi task allocation in mobile crowd sensing"
    - "from competition to complementarity comparative influence diffusion and maximization"

Connected through hyperedge HE-4 (induced by source paper "on budget feasible mechanism design for symmetric submodular objectives"):
  with members:
    - "simple and efficient budget feasible mechanisms for monotone submodular valuations"
    - "budget feasible mechanisms on matroids"
    - "adaptivity gaps for stochastic probing submodular and xos functions"
    - "coverage matching and beyond new results on budgeted mechanism design"
    - "bayesian budget feasibility with posted pricing"
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "budget feasible mechanisms for experimental design"
    - "budget feasible mechanism design from prior free to bayesian"

Connected through hyperedge HE-5 (induced by source paper "applications of economic and pricing models for wireless network security a survey"):
  with members:
    - "charging and discharging of plug in electric vehicles pevs in vehicle to grid v2g systems a cyber insurance based model"
    - "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"
    - "improving physical layer security in wireless communications using diversity techniques"
    - "a multitier wireless spectrum sharing system leveraging secure spectrum auctions"
    - "decomposition by partial linearization parallel optimization of multi agent systems"
    - "principles of physical layer security in multiuser wireless networks a survey"
    - "ps trust provably secure solution for truthful double spectrum auctions"
    - "truthful mechanisms for secure communication in wireless cooperative system"

Connected through hyperedge HE-6 (induced by source paper "information gathering with peers submodular optimization with peer prediction constraints"):
  with members:
    - "incentivizing evaluation via limited access to ground truth peer prediction makes things worse"
    - "a framework for designing information elicitation mechanisms that reward truth telling"
    - "learning to incentivize eliciting effort via output agreement"
    - "crowd access path optimization diversity matters"
    - "information gathering in networks via active exploration"
    - "submodular optimization with submodular cover and submodular knapsack constraints"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "a utility framework for bounded loss market makers"

Connected through hyperedge HE-7 (induced by source paper "data collection and wireless communication in internet of things iot using economic analysis and pricing models a survey"):
  with members:
    - "economics of internet of things iot an information market approach"
    - "target tracking via crowdsourcing a mechanism design approach"
    - "non stationary resource allocation policies for delay constrained video streaming application to video over internet of things enabled networks"
    - "from participatory sensing to mobile crowd sensing"
    - "machine learning in wireless sensor networks algorithms strategies and applications"
    - "motivating smartphone collaboration in data acquisition and distributed computing"
    - "fairness and social welfare in incentivizing participatory sensing"
    - "optimal pricing and load sharing for energy saving in communications cooperation"

Connected through hyperedge HE-8 (induced by source paper "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"):
  with members:
    - "budget feasible mechanisms for experimental design"
    - "budget feasible mechanism design from prior free to bayesian"
    - "budget feasible mechanisms"
    - "online stochastic packing applied to display ad allocation"

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)