human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a survey on truth discovery"
  Abstract: Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, for the same object, there usually exist conflicts among the collected multi-source information. To tackle this challenge, truth discovery, which integrates multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this survey, we focus on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects. We also discuss some future directions of truth discovery research. We hope that this survey will promote a better understanding of the current progress on truth discovery, and offer some guidelines on how to apply these approaches in application domains.

Connected through hyperedge HE-1 (induced by source paper "joint inference on truth rumor and their sources in social networks"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "automatic stance detection using end to end memory networks"
    - "necessary and sufficient budgets in information source finding with querying adaptivity gap"
    - "gemsec graph embedding with self clustering"
    - "on measuring bias in online information"
    - "fake news detection on social media a data mining perspective"
    - "rumor source detection under querying with untruthful answers"
    - "csi a hybrid deep model for fake news detection"

Connected through hyperedge HE-2 (induced by source paper "data market platforms trading data assets to solve data problems vision paper"):
  with members:
    - "efficient task specific data valuation for nearest neighbor algorithms"
    - "revenue maximization for query pricing"
    - "a marketplace for data an algorithmic solution"
    - "truth finding on the deep web is the problem solved"
    - "a theory of pricing private data"

Connected through hyperedge HE-3 (induced by source paper "a data dependent algorithm for querying earth mover s distance with low doubling dimension"):
  with members:
    - "conditional hardness of earth mover distance"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "optimal transport for domain adaptation"
    - "on constant factor approximation for earth mover distance over doubling metrics"
    - "fast construction of nets in low dimensional metrics and their applications"

Connected through hyperedge HE-4 (induced by source paper "gpm a generic probabilistic model to recover annotator s behavior and ground truth labeling"):
  with members:
    - "recover subjective quality scores from noisy measurements"

Connected through hyperedge HE-5 (induced by source paper "detecting toxicity in news articles application to bulgarian"):
  with members:
    - "hunting for troll comments in news community forums"
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "predicting the leading political ideology of youtube channels using acoustic textual and metadata information"
    - "fact checking meets fauxtography verifying claims about images"
    - "fine grained analysis of propaganda in news articles"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "all in one multi task learning for rumour verification"

Connected through hyperedge HE-6 (induced by source paper "it takes nine to smell a rat neural multi task learning for check worthiness prediction"):
  with members:
    - "in search of credible news"
    - "multi task ordinal regression for jointly predicting the trustworthiness and the leading political ideology of news media"
    - "fine grained analysis of propaganda in news articles"
    - "semeval 2019 task 8 fact checking in community question answering forums"
    - "automated fact checking task formulations methods and future directions"
    - "predicting factuality of reporting and bias of news media sources"
    - "fever a large scale dataset for fact extraction and verification"
    - "multi view models for political ideology detection of news articles"

Connected through hyperedge HE-7 (induced by source paper "an unsupervised bayesian neural network for truth discovery"):
  with members:
    - "sequential multi class labeling in crowdsourcing"
    - "robust decentralized detection and social learning in tandem networks"
    - "whose opinion to follow in multihypothesis social learning a large deviations perspective"
    - "generalized denoising auto encoders as generative models"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-8 (induced by source paper "crowdsourced truth discovery in the presence of hierarchies for knowledge fusion"):
  with members:
    - "clamshell speeding up crowds for low latency data labeling"
    - "truth finding on the deep web is the problem solved"
    - "from data fusion to knowledge fusion"
    - "crowder crowdsourcing entity resolution"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Question: Please tell me which class the center node 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)