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 "prediction based task assignment on spatial crowdsourcing"):
  Abstract: With the rapid development of mobile devices, the spatial crowdsourcing has recently attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to a system that automatically assigns a number of location-based workers with spatial tasks nearby . Previous works on the spatial crowdsourcing usually designed task assignment strategies that maximize some assignment scores. However, their assignment strategies only considered the existing workers and tasks in the spatial crowdsourcing system, which might achieve local optimality, due to the unavailability of future workers/tasks that may join the system. Thus, in this paper, our goal is to achieve globally optimal task assignments at the current timestamp, by taking into account not only the existing, but also those future workers/tasks. We formalize an important problem, namely prediction-based spatial crowdsourcing (PB-SC), which finds a global optimal strategy for worker-and-task assignments for multiple rounds, based on both existing and predicted task/worker locations, such that the total assignment quality score of multiple rounds is maximized, under the constraint of the travel budget. The PB-SC problem is very challenging, in terms of the prediction accuracy and efficiency. In this paper, we design an effective prediction method to estimate spatial distributions of workers and tasks in the future, and then utilize the predicted ones in our procedure of worker-and-task assignments. We prove that, the PB-SC problem is NP-hard, and thus intractable. Therefore, we propose efficient approximation algorithms, greedy and divide-and-conquer, to deal with the PB-SC problem, by considering both current and future task/worker distributions. Through extensive experiments, we demonstrate the efficiency and effectiveness of our PB-SC processing approaches on real/synthetic data.

Includes members:
  Member "prediction based task assignment in spatial crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities")
  Member "task assignment on multi skill oriented spatial crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities")
  Member "reliable diversity based spatial crowdsourcing by moving workers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "task allocation in mobile crowd sensing state of the art and future opportunities")

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.DB(Databases)