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: "data fusion resolving conflicts from multiple sources"
  Abstract: Many data management applications, such as setting up Web portals, managing enterprise data, managing community data, and sharing scientific data, require integrating data from multiple sources. Each of these sources provides a set of values and different sources can often provide conflicting values. To present quality data to users, it is critical to resolve conflicts and discover values that reflect the real world; this task is called {\em data fusion}. This paper describes a novel approach that finds true values from conflicting information when there are a large number of sources, among which some may copy from others. We present a case study on real-world data showing that the described algorithm can significantly improve accuracy of truth discovery and is scalable when there are a large number of data sources.

Connected through hyperedge HE-1 (induced by source paper "combining crowd and machines for multi predicate item screening"):
  with members:
    - "crowd based multi predicate screening of papers in literature reviews"
    - "crowdsourcing paper screening in systematic literature reviews"
    - "optimality of belief propagation for crowdsourced classification"
    - "crowdsourcing information extraction for biomedical systematic reviews"
    - "crowd access path optimization diversity matters"
    - "truelabel confusions a spectrum of probabilistic models in analyzing multiple ratings"
    - "crowder crowdsourcing entity resolution"

Connected through hyperedge HE-2 (induced by source paper "functional dependencies unleashed for scalable data exchange"):
  with members:
    - "finite open world query answering with number restrictions"
    - "finite open world query answering with number restrictions"
    - "the data exchange chase under the microscope"

Connected through hyperedge HE-3 (induced by source paper "less is more building selective anomaly ensembles"):
  with members:
    - "graph based anomaly detection and description a survey"
    - "a new local distance based outlier detection approach for scattered real world data"

Connected through hyperedge HE-4 (induced by source paper "online query scheduling on source permutation for big data integration"):
  with members:
    - "an analysis of structured data on the web"

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)