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: "materialized view selection by query clustering in xml data warehouses"
  Abstract: XML data warehouses form an interesting basis for decision-support applications that exploit complex data. However, native XML database management systems currently bear limited performances and it is necessary to design strategies to optimize them. In this paper, we propose an automatic strategy for the selection of XML materialized views that exploits a data mining technique, more precisely the clustering of the query workload. To validate our strategy, we implemented an XML warehouse modeled along the XCube specifications. We executed a workload of XQuery decision-support queries on this warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when queries are complex.

Connected through hyperedge HE-1 (induced by source paper "warehousing complex data from the web"):
  with members:
    - "an architecture framework for complex data warehouses"
    - "clustering based materialized view selection in data warehouses"
    - "un index de jointure pour les entrep ots de donn ees xml"

Connected through hyperedge HE-2 (induced by source paper "indices in xml databases"):
  with members:
    - "un index de jointure pour les entrep ots de donn ees xml"

Connected through hyperedge HE-3 (induced by source paper "x wacoda an xml based approach for warehousing and analyzing complex data"):
  with members:
    - "indices in xml databases"
    - "warehousing complex data from the web"
    - "a complex data warehouse for personalized anticipative medicine"
    - "a join index for xml data warehouses"
    - "biomedical data warehouses"
    - "an architecture framework for complex data warehouses"

Connected through hyperedge HE-4 (induced by source paper "a case for a collaborative query management system"):
  with members:
    - "clustering based materialized view selection in data warehouses"

Connected through hyperedge HE-5 (induced by source paper "a join index for xml data warehouses"):
  with members:
    - "an architecture framework for complex data warehouses"

Connected through hyperedge HE-6 (induced by source paper "data mining based materialized view and index selection in data warehouses"):
  with members:
    - "frequent itemsets mining for database auto administration"
    - "clustering based materialized view selection in data warehouses"
    - "automatic selection of bitmap join indexes in data warehouses"

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)