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 efficient and balanced platform for data parallel subsampling workloads"
  Abstract: With the advent of internet services, data started growing faster than it can be processed. To personalize user experience, this enormous data has to be processed in real time, in interactive fashion. In order to achieve faster data processing often a statistical method called subsampling. Subsampling workloads compute statistics from a random subset of sample data (i.e., a subsample). Data-parallel platforms group these samples into tasks; each task subsamples its data in parallel. #R##N#Current, state-of-the-art platforms such as Hadoop are built for large tasks that run for long periods of time, but applications with smaller average task sizes suffer large overheads on these platforms. Tasks in subsampling workloads are sized to minimize the number of overall cache misses, and these tasks can complete in seconds. This technique can reduce the overall length of a map-reduce job, but only when the savings from the cache miss rate reduction are not eclipsed by the platform overhead of task creation and data distribution. #R##N#In this thesis, we propose a data-parallel platform with an efficient data distribution component that breaks data-parallel subsampling workloads into compute clusters with tiny tasks. Each tiny task completes in few hundreds of milliseconds to seconds. Tiny tasks reduce processor cache misses caused by random subsampling, which speeds up per-task running time. However, they cause significant scheduling overheads and data distribution challenges. We propose a task knee-pointing algorithm and a dynamic scheduler that schedules the tasks to worker nodes based on the availability and response times of the data nodes. We compare our framework against various configurations of BashReduce and Hadoop. A detailed discussion of tiny task approach on two workloads, EAGLET and Netflix movie rating is presented.

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)