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: "on the relative trust between inconsistent data and inaccurate constraints"
  Abstract: Functional dependencies (FDs) specify the intended data semantics while violations of FDs indicate deviation from these semantics. In this paper, we study a data cleaning problem in which the FDs may not be completely correct, e.g., due to data evolution or incomplete knowledge of the data semantics. We argue that the notion of relative trust is a crucial aspect of this problem: if the FDs are outdated, we should modify them to fit the data, but if we suspect that there are problems with the data, we should modify the data to fit the FDs. In practice, it is usually unclear how much to trust the data versus the FDs. To address this problem, we propose an algorithm for generating non-redundant solutions (i.e., simultaneous modifications of the data and the FDs) corresponding to various levels of relative trust. This can help users determine the best way to modify their data and/or FDs to achieve consistency.

Connected through hyperedge HE-1 (induced by source paper "complexity and efficient algorithms for data inconsistency evaluating and repairing"):
  with members:
    - "a formal framework for probabilistic unclean databases"
    - "computing optimal repairs for functional dependencies"
    - "a near optimal sublinear time algorithm for approximating the minimum vertex cover size"
    - "minimal change integrity maintenance using tuple deletions"

Connected through hyperedge HE-2 (induced by source paper "a hybrid data cleaning framework using markov logic networks"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"
    - "complexity and approximation of fixing numerical attributes in databases under integrity constraints"

Connected through hyperedge HE-3 (induced by source paper "impacts of dirty data and experimental evaluation"):
  with members:
    - "auto weka combined selection and hyperparameter optimization of classification algorithms"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-4 (induced by source paper "holoclean holistic data repairs with probabilistic inference"):
  with members:
    - "rapidly mixing gibbs sampling for a class of factor graphs using hierarchy width"
    - "truth finding on the deep web is the problem solved"
    - "slimfast guaranteed results for data fusion and source reliability"
    - "incremental knowledge base construction using deepdive"
    - "hinge loss markov random fields and probabilistic soft logic"
    - "dimmwitted a study of main memory statistical analytics"
    - "uniform solution sampling using a constraint solver as an oracle"
    - "tuffy scaling up statistical inference in markov logic networks using an rdbms"

Connected through hyperedge HE-5 (induced by source paper "bayeswipe a scalable probabilistic framework for cleaning bigdata"):
  with members:
    - "crowder crowdsourcing entity resolution"
    - "data cleaning and query answering with matching dependencies and matching functions"

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)