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 "frog a fast and reliable crowdsourcing framework technical report"):
  Abstract: For decades, the crowdsourcing has gained much attention from both academia and industry, which outsources a number of tasks to human workers. Existing works considered improving the task accuracy through voting or learning methods, they usually did not fully take into account reducing the latency of the task completion. When a task requester posts a group of tasks (e.g., sentiment analysis), and one can only obtain answers of all tasks after the last task is accomplished. As a consequence, the time delay of even one task in this group could delay the next step of the task requester's work from minutes to days, which is quite undesirable for the task requester. #R##N#Inspired by the importance of the task accuracy and latency, in this paper, we will propose a novel crowdsourcing framework, namely Fast and Reliable crOwdsourcinG framework (FROG), which intelligently assigns tasks to workers, such that the latencies of tasks are reduced and the expected accuracies of tasks are met. Specifically, our FROG framework consists of two important components, task scheduler and notification modules. For the task scheduler module, we formalize a FROG task scheduling (FROG-TS) problem, in which the server actively assigns workers with tasks with high reliability and low latency. We prove that the FROG-TS problem is NP-hard. Thus, we design two heuristic approaches, request-based and batch-based scheduling. For the notification module, we define an efficient worker notifying (EWN) problem, which only sends task invitations to those workers with high probabilities of accepting the tasks. To tackle the EWN problem, we propose a smooth kernel density estimation approach to estimate the probability that a worker accepts the task invitation. Through extensive experiments, we demonstrate the effectiveness and efficiency of our proposed FROG platform on both real and synthetic data sets.

Includes members:
  Member "octopus a framework for cost quality time optimization in crowdsourcing":
    also connected through other hyperedges:
      (none)
  Member "clamshell speeding up crowds for low latency data labeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "finish them pricing algorithms for human computation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crowd powered data mining")
  Member "crowdsourcing a word emotion association lexicon":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "cdas a crowdsourcing data analytics system":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the disruptions of 5g on data driven technologies and applications")
  Member "crowder crowdsourcing entity resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crowdsourced collective entity resolution with relational match propagation")
  Member "whom to ask jury selection for decision making tasks on micro blog services":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "crowd powered data mining")
  Member "analytic methods for optimizing realtime crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "auditing news curation systems a case study examining algorithmic and editorial logic in apple news")

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)