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 "crowd powered data mining"):
  Abstract: Many data mining tasks cannot be completely addressed by auto- mated processes, such as sentiment analysis and image classification. Crowdsourcing is an effective way to harness the human cognitive ability to process these machine-hard tasks. Thanks to public crowdsourcing platforms, e.g., Amazon Mechanical Turk and Crowd- Flower, we can easily involve hundreds of thousands of ordinary workers (i.e., the crowd) to address these machine-hard tasks. In this tutorial, we will survey and synthesize a wide spectrum of existing studies on crowd-powered data mining. We first give an overview of crowdsourcing, and then summarize the fundamental techniques, including quality control, cost control, and latency control, which must be considered in crowdsourced data mining. Next we review crowd-powered data mining operations, including classification, clustering, pattern mining, machine learning using the crowd (including deep learning, transfer learning and semi-supervised learning) and knowledge discovery. Finally, we provide the emerging challenges in crowdsourced data mining.

Includes members:
  Member "crowdsourced correlation clustering with relative distance comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "multi label annotation aggregation in crowdsourcing":
    also connected through other hyperedges:
      (none)
  Member "clustering via crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed clustering in the anonymized space with local differential privacy")
  Member "clamshell speeding up crowds for low latency data labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flame a self adaptive auto labeling system for heterogeneous mobile processors")
  Member "estimating the impact of unknown unknowns on aggregate query results":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample debiasing in the themis open world database system extended version")
  Member "comprehensive and reliable crowd assessment algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metrology for ai from benchmarks to instruments")
  Member "finish them pricing algorithms for human computation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "octopus a framework for cost quality time optimization in crowdsourcing")
  Member "leveraging transitive relations for crowdsourced joins":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "crowdsourced collective entity resolution with relational match propagation")

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)