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 "specialty aware task assignment in spatial crowdsourcing"):
  Abstract: With the rapid development of Mobile Internet, spatial crowdsourcing is gaining more and more attention from both academia and industry. #R##N#In spatial crowdsourcing, spatial tasks are sent to workers based on their locations. #R##N#A wide kind of tasks in spatial crowdsourcing are specialty-aware, which are complex and need to be completed by workers with different skills collaboratively. #R##N#Existing studies on specialty-aware spatial crowdsourcing assume that each worker has a united charge when performing different tasks, no matter how many skills of her/him are used to complete the task, which is not fair and practical. #R##N#In this paper, we study the problem of specialty-aware task assignment in spatial crowdsourcing, where each worker has fine-grained charge for each of their skills, and the goal is to maximize the total number of completed tasks based on tasks' budget and requirements on particular skills. #R##N#The problem is proven to be NP-hard. Thus, we propose two efficient heuristics to solve the problem. #R##N#Experiments on both synthetic and real datasets demonstrate the effectiveness and efficiency of our solutions.

Includes members:
  Member "a real time framework for task assignment in hyperlocal spatial crowdsourcing":
    also connected through other hyperedges:
      (none)
  Member "task assignment on multi skill oriented 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 "capacitated team formation problem on social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algorithms for fair team formation in online labour marketplaces")

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)