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: "configurable strategies for work stealing"
  Abstract: Work-stealing systems are typically oblivious to the nature of the tasks they#R##N#are scheduling. For instance, they do not know or take into account how long a#R##N#task will take to execute or how many subtasks it will spawn. Moreover, the#R##N#actual task execution order is typically determined by the underlying task#R##N#storage data structure, and cannot be changed. There are thus possibilities for#R##N#optimizing task parallel executions by providing information on specific tasks#R##N#and their preferred execution order to the scheduling system.#R##N#  We introduce scheduling strategies to enable applications to dynamically#R##N#provide hints to the task-scheduling system on the nature of specific tasks.#R##N#Scheduling strategies can be used to independently control both local task#R##N#execution order as well as steal order. In contrast to conventional scheduling#R##N#policies that are normally global in scope, strategies allow the scheduler to#R##N#apply optimizations on individual tasks. This flexibility greatly improves#R##N#composability as it allows the scheduler to apply different, specific#R##N#scheduling choices for different parts of applications simultaneously. We#R##N#present a number of benchmarks that highlight diverse, beneficial effects that#R##N#can be achieved with scheduling strategies. Some benchmarks (branch-and-bound,#R##N#single-source shortest path) show that prioritization of tasks can reduce the#R##N#total amount of work compared to standard work-stealing execution order. For#R##N#other benchmarks (triangle strip generation) qualitatively better results can#R##N#be achieved in shorter time. Other optimizations, such as dynamic merging of#R##N#tasks or stealing of half the work, instead of half the tasks, are also shown#R##N#to improve performance. Composability is demonstrated by examples that combine#R##N#different strategies, both within the same kernel (prefix sum) as well as when#R##N#scheduling multiple kernels (prefix sum and unbalanced tree search).

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)