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: "on the scalability of multidimensional databases"
  Abstract: It is commonly accepted in the practice of on-line analytical processing of databases that the multidimensional database organization is less scalable than the relational one. It is easy to see that the size of the multidimensional organization may increase very quickly. For example, if we introduce one additional dimension, then the total number of possible cells will be at least doubled. However, this reasoning does not takethe fact into account that the multidimensional organization can be compressed. There are compression techniques, which can remove all or at least a part of the empty cells from the multidimensional organization, while maintaining a good retrieval performance. Relational databases often use B-tree indices to speed up the access to given rows of tables. It can be proven, under some reasonable assumptions, that the total size of the table and the B-tree index is bigger than a compressed multidimensional representation. This implies that the compressed array results in a smaller database and faster access at the same time. This paper compares several compression techniques and shows when we should and should not apply compressed arrays instead of relational tables.

Connected through hyperedge HE-1 (induced by source paper "difference huffman coding of multidimensional databases"):
  with members:
    - "difference sequence compression of multidimensional databases"
    - "multidimensional or relational how to organize an on line analytical processing database"
    - "attribute value reordering for efficient hybrid olap"

Connected through hyperedge HE-2 (induced by source paper "difference sequence compression of multidimensional databases"):
  with members:
    - "multidimensional or relational how to organize an on line analytical processing database"

Connected through hyperedge HE-3 (induced by source paper "caching in multidimensional databases"):
  with members:
    - "difference sequence compression of multidimensional databases"
    - "attribute value reordering for efficient hybrid 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)