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 "automatic weighted matching rectifying rule discovery for data repairing"):
  Abstract: Data repairing is a key problem in data cleaning which aims to uncover and rectify data errors. Traditional methods depend on data dependencies to check the existence of errors in data, but they fail to rectify the errors. To overcome this limitation, recent methods define repairing rules on which they depend to detect and fix errors. However, all existing data repairing rules are provided by experts which is an expensive task in time and effort. Besides, rule-based data repairing methods need an external verified data source or user verifications; otherwise they are incomplete where they can repair only a small number of errors. In this paper, we define weighted matching rectifying rules (WMRRs) based on similarity matching to capture more errors. We propose a novel algorithm to discover WMRRs automatically from dirty data in-hand. We also develop an automatic algorithm for rules inconsistency resolution. Additionally, based on WMRRs, we propose an automatic data repairing algorithm (WMRR-DR) which uncovers a large number of errors and rectifies them dependably. We experimentally verify our method on both real-life and synthetic data. The experimental results prove that our method can discover effective WMRRs from the dirty data in-hand, and perform dependable and full-automatic repairing based on the discovered WMRRs, with higher accuracy than the existing dependable methods.

Includes members:
  Member "holoclean holistic data repairs with probabilistic inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmark and survey of automated machine learning frameworks")
  Member "incremental knowledge base construction using deepdive":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint reasoning for multi faceted commonsense knowledge")
  Member "hinge loss markov random fields and probabilistic soft logic":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational neural machines")
  Member "tuffy scaling up statistical inference in markov logic networks using an rdbms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")

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)