human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient resource oblivious algorithms for multicores"):
  Abstract: We consider the design of efficient algorithms for a multicore computing environment with a global shared memory and p cores, each having a cache of size M, and with data organized in blocks of size B. We characterize the class of `Hierarchical Balanced Parallel (HBP)' multithreaded computations for multicores. HBP computations are similar to the hierarchical divide & conquer algorithms considered in recent work, but have some additional features that guarantee good performance even when accounting for the cache misses due to false sharing. Most of our HBP algorithms are derived from known cache-oblivious algorithms with high parallelism, however we incorporate new techniques that reduce the effect of false-sharing. #R##N#Our approach to addressing false sharing costs (or more generally, block misses) is to ensure that any task that can be stolen shares O(1) blocks with other tasks. We use a gapping technique for computations that have larger than O(1) block sharing. We also incorporate the property of limited access writes analyzed in a companion paper, and we bound the cost of accessing shared blocks on the execution stacks of tasks. #R##N#We present the Priority Work Stealing (PWS) scheduler, and we establish that, given a sufficiently `tall' cache, PWS deterministically schedules several highly parallel HBP algorithms, including those for scans, matrix computations and FFT, with cache misses bounded by the sequential complexity, when accounting for both traditional cache misses and for false sharing. We also present a list ranking algorithm with almost optimal bounds. PWS schedules without using cache or block size information, and uses knowledge of processors only to the extent of determining the available locations from which tasks may be stolen; thus it schedules resource-obliviously.

Includes members:
  Member "resource oblivious sorting on multicores":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal parallel algorithms in the binary forking model")
  Member "analysis of randomized work stealing with false sharing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bringing inter thread cache benefits to federated scheduling extended results technical report")

Question: Please tell me which class the source paper 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)