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 "truth discovery via proxy voting"):
  Abstract: Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we design simple truth discovery methods inspired by \emph{proxy voting}, that give higher weight to workers whose answers are close to those of other workers. #R##N#We prove that under standard statistical assumptions, proxy-based truth discovery (\PTD) allows us to estimate the true competence of each worker, whether workers face questions whose answers are real-valued, categorical, or rankings. We then demonstrate through extensive empirical study on synthetic and real data that \PTD is substantially better than unweighted aggregation, and competes well with other truth discovery methods, in all of the above domains.

Includes members:
  Member "a survey on truth discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint inference on truth rumor and their sources in social networks")
  Member "truth discovery algorithms an experimental evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "restricted boltzmann machines for robust and fast latent truth discovery")
  Member "double or nothing multiplicative incentive mechanisms for crowdsourcing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "prospect theory based crowdsourcing for classification in the presence of spammers")
  Member "common voting rules as maximum likelihood estimators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")

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.AI(Artificial Intelligence)