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 "pricing mechanisms for crowd sensed spatial statistics based radio mapping"):
  Abstract: Networking on white spaces (i.e., locally unused spectrum) relies on active monitoring of spectrum usage. Spectrum databases based on empirical radio propagation models are widely adopted but shown to be error-prone, since they do not account for built environments (e.g., trees and man-made buildings). As an alternative, crowd-sensed radio mapping by mobile clients who acquire local spectrum data and transmit it to a central aggregator (platform) for processing, results in more accurate radio maps. Success of such crowd-sensing systems presumes some incentive mechanisms to attract user participation. In this paper, we assume that the platform who constructs radio environment maps makes one-time offers (the incentive for participation) to users, and collects data from those who accept the offers. We design pricing mechanisms based on expected utility (EU) maximization, where EU captures the tradeoff between radio mapping performance (location and data quality), crowd-sensing cost, and uncertainty in offer outcomes (i.e., possible expiration and rejection). Specifically, we consider both  sequential offering , where one best price offer is sent to the best user in each round, and  batched offering , where a batch of offers is made in each round. For the later, we show that EU is  submodular  in the discrete domain, and propose a mechanism that first fixes the pricing rule, and selects users based on unconstrained submodular maximization (USM); it then compares different pricing rules to find the best batch of offers in each round. We show that USM-based user selection has provable performance guarantee. Proposed mechanisms are evaluated and compared against utility-maximization-based baseline mechanisms.

Includes members:
  Member "crowdsourcing with tullock contests a new perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "small profits and quick returns an incentive mechanism design for iot based crowdsourcing under continuous platform competition")
  Member "capacity considerations for secondary networks in tv white space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sas assisted coexistence aware dynamic channel assignment in cbrs band")

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)