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: "task assignment on multi skill oriented spatial crowdsourcing"
  Abstract: With the rapid development of mobile devices and crowdsourcing platforms, the spatial crowdsourcing has attracted much attention from the database community. Specifically, the spatial crowdsourcing refers to sending location-based requests to workers, based on their current positions. In this paper, we consider a spatial crowdsourcing scenario, in which each worker has a set of qualified skills, whereas each spatial task (e.g., repairing a house, decorating a room, and performing entertainment shows for a ceremony) is time-constrained, under the budget constraint, and required a set of skills. Under this scenario, we will study an important problem, namely  multi-skill spatial crowdsourcing  (MS-SC), which finds an optimal worker-and-task assignment strategy, such that skills between workers and tasks match with each other, and workers’ benefits are maximized under the budget constraint. We prove that the MS-SC problem is NP-hard and intractable. Therefore, we propose three effective heuristic approaches, including greedy,   $g$      -divide-and-conquer and cost-model-based adaptive algorithms to get worker-and-task assignments. Through extensive experiments, we demonstrate the efficiency and effectiveness of our MS-SC processing approaches on both real and synthetic data sets.

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"
    - "taskme multi task allocation in mobile crowd sensing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "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 "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"
    - "fundamental limits of budget fidelity trade off in label crowdsourcing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "budget optimal task allocation for reliable crowdsourcing systems"

Connected through hyperedge HE-3 (induced by source paper "specialty aware task assignment in spatial crowdsourcing"):
  with members:
    - "a real time framework for task assignment in hyperlocal spatial crowdsourcing"
    - "capacitated team formation problem on social networks"

Connected through hyperedge HE-4 (induced by source paper "in route task selection in crowdsourcing"):
  with members:
    - "reliable diversity based spatial crowdsourcing by moving workers"

Connected through hyperedge HE-5 (induced by source paper "a real time framework for task assignment in hyperlocal spatial crowdsourcing"):
  with members:
    - "taskme multi task allocation in mobile crowd sensing"
    - "reliable diversity based spatial crowdsourcing by moving workers"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "prediction based task assignment in spatial crowdsourcing"):
  with members:
    - "reliable diversity based spatial crowdsourcing by moving workers"

Connected through hyperedge HE-7 (induced by source paper "task assignment on spatial crowdsourcing experiments and analyses technical report"):
  with members:
    - "task assignment on spatial crowdsourcing technical report"
    - "reliable diversity based spatial crowdsourcing by moving workers"

Connected through hyperedge HE-8 (induced by source paper "task assignment on spatial crowdsourcing technical report"):
  with members:
    - "reliable diversity based spatial crowdsourcing by moving workers"

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)