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: "clustering based materialized view selection in data warehouses"
  Abstract: Materialized view selection is a non-trivial task. Hence, its complexity must be reduced. A judicious choice of views must be cost-driven and influenced by the workload experienced by the system. In this paper, we propose a framework for materialized view selection that exploits a data mining technique (clustering), in order to determine clusters of similar queries. We also propose a view merging algorithm that builds a set of candidate views, as well as a greedy process for selecting a set of views to materialize. This selection is based on cost models that evaluate the cost of accessing data using views and the cost of storing these views. To validate our strategy, we executed a workload of decision-support queries on a test data warehouse, with and without using our strategy. Our experimental results demonstrate its efficiency, even when storage space is limited.

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

Connected through hyperedge HE-2 (induced by source paper "query performance optimization in xml data warehouses"):
  with members:
    - "an architecture framework for complex data warehouses"

Connected through hyperedge HE-3 (induced by source paper "fragmenting very large xml data warehouses via k means clustering algorithm"):
  with members:
    - "enhancing xml data warehouse query performance by fragmentation"
    - "dynamic index selection in data warehouses"
    - "expressing olap operators with the tax xml algebra"
    - "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:
    - "materialized view selection by query clustering in xml data warehouses"

Connected through hyperedge HE-5 (induced by source paper "data mining based fragmentation of xml data warehouses"):
  with members:
    - "dynamic index selection in data warehouses"
    - "expressing olap operators with the tax xml algebra"

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

Connected through hyperedge HE-7 (induced by source paper "a comparison of five probabilistic view size estimation techniques in olap"):
  with members:
    - "unasssuming view size estimation techniques in olap"

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)