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: "truth discovery algorithms an experimental evaluation"
  Abstract: A fundamental problem in data fusion is to determine the veracity of multi-source data in order to resolve conflicts. While previous work in truth discovery has proved to be useful in practice for specific settings, sources' behavior or data set characteristics, there has been limited systematic comparison of the competing methods in terms of efficiency, usability, and repeatability. We remedy this deficit by providing a comprehensive review of 12 state-of-the art algorithms for truth discovery. We provide reference implementations and an in-depth evaluation of the methods based on extensive experiments on synthetic and real-world data. We analyze aspects of the problem that have not been explicitly studied before, such as the impact of initialization and parameter setting, convergence, and scalability. We provide an experimental framework for extensively comparing the methods in a wide range of truth discovery scenarios where source coverage, numbers and distributions of conflicts, and true positive claims can be controlled and used to evaluate the quality and performance of the algorithms. Finally, we report comprehensive findings obtained from the experiments and provide new insights for future research.

Connected through hyperedge HE-1 (induced by source paper "truth discovery via proxy voting"):
  with members:
    - "a survey on truth discovery"
    - "double or nothing multiplicative incentive mechanisms for crowdsourcing"
    - "common voting rules as maximum likelihood estimators"

Connected through hyperedge HE-2 (induced by source paper "restricted boltzmann machines for robust and fast latent truth discovery"):
  with members:
    - "a survey on truth discovery"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-3 (induced by source paper "truth validation with evidence"):
  with members:
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-4 (induced by source paper "unsupervised string transformation learning for entity consolidation"):
  with members:
    - "neural guided deductive search for real time program synthesis from examples"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "incremental knowledge base construction using deepdive"
    - "learning semantic string transformations from examples"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-5 (induced by source paper "smartmtd a graph based approach for effective multi truth discovery"):
  with members:
    - "fusing data with correlations"
    - "a bayesian approach to discovering truth from conflicting sources for data integration"

Connected through hyperedge HE-6 (induced by source paper "ice information credibility evaluation on social media via representation learning"):
  with members:
    - "deep learning over multi field categorical data a case study on user response prediction"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "distributed representations of words and phrases and their compositionality"
    - "bpr bayesian personalized ranking from implicit feedback"
    - "representation learning a review and new perspectives"

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)