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: "partitioning graph databases a quantitative evaluation"
  Abstract: Electronic data is growing at increasing rates, in both size and connectivity: the increasing presence of, and interest in, relationships between data. An example is the Twitter social network graph. Due to this growth demand is increasing for technologies that can process such data. Currently relational databases are the predominant technology, but they are poorly suited to processing connected data as they are optimized for index-intensive operations. Conversely, graph databases are optimized for graph computation. They link records by direct references, avoiding index lookups, and enabling retrieval of adjacent elements in constant time, regardless of graph size. However, as data volume increases these databases outgrow the resources of one computer and data partitioning becomes necessary. We evaluate the viability of using graph partitioning algorithms to partition graph databases. A prototype partitioned database was developed. Three partitioning algorithms explored and one implemented. Three graph datasets were used: two real and one synthetically generated. These were partitioned in various ways and the impact on database performance measured. We defined one synthetic access pattern per dataset and executed each on the partitioned datasets. Evaluation took place in a simulation environment, ensuring repeatability and allowing measurement of metrics like network traffic and load balance. Results show that compared to random partitioning the partitioning algorithm reduced traffic by 40-90%. Executing the algorithm intermittently during usage maintained partition quality, while requiring only 1% the computation of initial partitioning. Strong correlations were found between theoretic quality metrics and generated network traffic under non-uniform access patterns.

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)