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: "reliable diversity based spatial crowdsourcing by moving workers"
  Abstract: With the rapid development of mobile devices and the crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request to workers according to their positions. In this paper, we consider an important spatial crowdsourcing problem, namely reliable diversity-based spatial crowdsourcing (RDB-SC), in which spatial tasks (such as taking videos/photos of a landmark or firework shows, and checking whether or not parking spaces are available) are time-constrained, and workers are moving towards some directions. Our RDB-SC problem is to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized. We prove that the RDB-SC problem is NP-hard and intractable. Thus, we propose three effective approximation approaches, including greedy, sampling, and divide-and-conquer algorithms. In order to improve the efficiency, we also design an effective cost-model-based index, which can dynamically maintain moving workers and spatial tasks with low cost, and efficiently facilitate the retrieval of RDB-SC answers. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches over both real and synthetic datasets.

Connected through hyperedge HE-1 (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"
    - "incentives for privacy tradeoff in community sensing"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-2 (induced by source paper "hytasker hybrid task allocation in mobile crowd sensing"):
  with members:
    - "task allocation in mobile crowd sensing state of the art and future opportunities"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "prediction based task assignment in spatial crowdsourcing"
    - "taskme multi task allocation in mobile crowd sensing"
    - "lazier than lazy greedy"
    - "data for development the d4d challenge on mobile phone data"

Connected through hyperedge HE-3 (induced by source paper "budget aware online task assignment in spatial crowdsourcing"):
  with members:
    - "prediction based task assignment in spatial crowdsourcing"
    - "achieving budget optimality with adaptive schemes in crowdsourcing"
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "fundamental limits of budget fidelity trade off in label crowdsourcing"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-4 (induced by source paper "in route task selection in crowdsourcing"):
  with members:
    - "task assignment on multi skill oriented spatial crowdsourcing"

Connected through hyperedge HE-5 (induced by source paper "next generation crowdsourcing for collective intelligence"):
  with members:
    - "the fundamentals of policy crowdsourcing"
    - "the contours of crowd capability"
    - "how to work a crowd developing crowd capital through crowdsourcing"
    - "crowd science measurements models and methods"
    - "prediction based task assignment on spatial crowdsourcing"
    - "wearable computing for health and fitness exploring the relationship between data and human behaviour"
    - "the theory of crowd capital"

Connected through hyperedge HE-6 (induced by source paper "a real time framework for task assignment in hyperlocal spatial crowdsourcing"):
  with members:
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "taskme multi task allocation in mobile crowd sensing"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-7 (induced by source paper "prediction based task assignment in spatial crowdsourcing"):
  with members:
    - "task assignment on multi skill oriented spatial crowdsourcing"

Connected through hyperedge HE-8 (induced by source paper "task assignment on spatial crowdsourcing experiments and analyses technical report"):
  with members:
    - "task assignment on multi skill oriented spatial crowdsourcing"
    - "task assignment on spatial crowdsourcing technical report"

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.DB(Databases)