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: "an architecture framework for complex data warehouses"
  Abstract: Nowadays, many decision support applications need to exploit data that are not only numerical or symbolic, but also multimedia, multistructure, multisource, multimodal, and/or multiversion. We term such data complex data. Managing and analyzing complex data involves a lot of different issues regarding their structure, storage and processing, and metadata are a key element in all these processes. Such problems have been addressed by classical data warehousing (i.e., applied to "simple" data). However, data warehousing approaches need to be adapted for complex data. In this paper, we first propose a precise, though open, definition of complex data. Then we present a general architecture framework for warehousing complex data. This architecture heavily relies on metadata and domain-related knowledge, and rests on the XML language, which helps storing data, metadata and domain-specific knowledge altogether, and facilitates communication between the various warehousing processes.

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"
    - "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 "warehousing complex archaeological objects"):
  with members:
    - "a complex data warehouse for personalized anticipative medicine"

Connected through hyperedge HE-3 (induced by source paper "pattern tree based xolap rollup operator for xml complex hierarchies"):
  with members:
    - "expressing olap operators with the tax xml algebra"

Connected through hyperedge HE-4 (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"
    - "materialized view selection by query clustering in xml data warehouses"
    - "a complex data warehouse for personalized anticipative medicine"
    - "a join index for xml data warehouses"
    - "biomedical data warehouses"

Connected through hyperedge HE-5 (induced by source paper "query performance optimization in xml data warehouses"):
  with members:
    - "clustering based materialized view selection in data warehouses"

Connected through hyperedge HE-6 (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"
    - "clustering based materialized view selection in data warehouses"

Connected through hyperedge HE-7 (induced by source paper "a join index for xml data warehouses"):
  with members:
    - "materialized view selection by query clustering in xml data warehouses"

Connected through hyperedge HE-8 (induced by source paper "benchmarking data warehouses"):
  with members:
    - "benchmarking oodbs with a generic tool"
    - "dweb a data warehouse engineering benchmark"

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)