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: "experimenting with bittorrent on a cluster a good or a bad idea"
  Abstract: Evaluation of large-scale network systems and applications is usually done in one of three ways: simulations, real deployment on Internet, or on an emulated network testbed such as a cluster. Simulations can study very large systems but often abstract out many practical details, whereas real world tests are often quite small, on the order of a few hundred nodes at most, but have very realistic conditions. Clusters and other dedicated testbeds offer a middle ground between the two: large systems with real application code. They also typically allow configuring the testbed to enable repeatable experiments. In this paper we explore how to run large BitTorrent experiments in a cluster setup. We have chosen BitTorrent because the source code is available and it has been a popular target for research. Our contribution is twofold. First, we show how to tweak and configure the BitTorrent client to allow for a maximum number of clients to be run on a single machine, without running into any physical limits of the machine. Second, our results show that the behavior of BitTorrent can be very sensitive to the configuration and we revisit some existing BitTorrent research and consider the implications of our findings on previously published results. As we show in this paper, BitTorrent can change its behavior in subtle ways which are sometimes ignored in published works.

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)