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 "ciuv collaborating information against unreliable views"):
  Abstract: In many real world applications, the information of an object can be obtained from multiple sources. The sources may provide different point of views based on their own origin. As a consequence, conflicting pieces of information are inevitable, which gives rise to a crucial problem: how to find the truth from these conflicts. Many truth-finding methods have been proposed to resolve conflicts based on information trustworthy (i.e. more appearance means more trustworthy) as well as source reliability. However, the factor of men's involvement, i.e., information may be falsified by men with malicious intension, is more or less ignored in existing methods. Collaborating the possible relationship between information's origins and men's participation are still not studied in research. To deal with this challenge, we propose a method -- Collaborating Information against Unreliable Views (CIUV) --- in dealing with men's involvement for finding the truth. CIUV contains 3 stages for interactively mitigating the impact of unreliable views, and calculate the truth by weighting possible biases between sources. We theoretically analyze the error bound of CIUV, and conduct intensive experiments on real dataset for evaluation. The experimental results show that CIUV is feasible and has the smallest error compared with other methods.

Includes members:
  Member "a bayesian approach to discovering truth from conflicting sources for data integration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "open knowledge enrichment for long tail entities")
  Member "how to grade a test without knowing the answers a bayesian graphical model for adaptive crowdsourcing and aptitude testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning latent parameters without human response patterns item response theory with artificial crowds")

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)