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: "evaluating hadoop clusters with tpcx hs"
  Abstract: The growing complexity and variety of Big Data platforms makes it both difficult and time consuming for all system users to properly setup and operate the systems. Another challenge is to compare the platforms in order to choose the most appropriate one for a particular application. All these factors motivate the need for a standardized Big Data benchmark that can help the users in the process of platform evaluation. Just recently TPCx-HS [1][2] has been released as the first standardized Big Data benchmark designed to stress test a Hadoop cluster. The goal of this study is to evaluate and compare how the network setup influences the performance of a Hadoop cluster. In particular, experiments were performed using shared and dedicated 1Gbit networks utilized by the same Cloudera Hadoop Distribution (CDH) cluster setup. The TPCx-HS benchmark, which is very network intensive, was used to stress test and compare both cluster setups. All the presented results are obtained by using the officially available version [1] of the benchmark, but they are not comparable with the officially reported results and are meant as an experimental evaluation, not audited by any external organization. As expected the dedicated 1Gbit network setup performed much faster than the shared 1Gbit setup. However, what was surprising is the negligible price difference between both cluster setups, which pays off with a multifold performance return.

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.DC(Distributed, Parallel, and Cluster Computing)