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 "crowdsourced truth discovery in the presence of hierarchies for knowledge fusion"):
  Abstract: Existing works for truth discovery in categorical data usually assume that claimed values are mutually exclusive and only one among them is correct. However, many claimed values are not mutually exclusive even for functional predicates due to their hierarchical structures. Thus, we need to consider the hierarchical structure to effectively estimate the trustworthiness of the sources and infer the truths. We propose a probabilistic model to utilize the hierarchical structures and an inference algorithm to find the truths. In addition, in the knowledge fusion, the step of automatically extracting information from unstructured data (e.g., text) generates a lot of false claims. To take advantages of the human cognitive abilities in understanding unstructured data, we utilize crowdsourcing to refine the result of the truth discovery. We propose a task assignment algorithm to maximize the accuracy of the inferred truths. The performance study with real-life datasets confirms the effectiveness of our truth inference and task assignment algorithms.

Includes members:
  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 "truth finding on the deep web is the problem solved":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "data market platforms trading data assets to solve data problems vision paper")
  Member "from data fusion to knowledge fusion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "knowledge federation hierarchy and unification")
  Member "a survey on truth discovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  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 "a bayesian approach to discovering truth from conflicting sources for data integration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "open knowledge enrichment for long tail entities")

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)