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 "providing high and controllable performance in multicore systems through shared resource management"):
  Abstract: Multiple applications executing concurrently on a multicore system interfere with each other at different shared resources such as main memory and shared caches. Such inter-application interference, if uncontrolled, results in high system performance degradation and unpredictable application slowdowns. While previous work has proposed application-aware memory scheduling as a solution to mitigate inter-application interference and improve system performance, previously proposed memory scheduling techniques incur high hardware complexity and unfairly slowdown some applications. Furthermore, previously proposed memory-interference mitigation techniques are not designed to precisely control application performance. #R##N#This dissertation seeks to achieve high and controllable performance in multicore systems by mitigating and quantifying the impact of shared resource interference. First, towards mitigating memory interference and achieving high performance, we propose the Blacklisting memory scheduler that achieves high performance and fairness at low complexity. Next, towards quantifying the impact of memory interference and achieving controllable performance in the presence of memory bandwidth interference, we propose the Memory Interference induced Slowdown Estimation (MISE) model. We propose and demonstrate two use cases that can leverage MISE to provide soft performance guarantees and high overall performance/fairness. Finally, we seek to quantify the impact of shared cache interference on application slowdowns, in addition to memory bandwidth interference. Towards this end, we propose the Application Slowdown Model (ASM). We propose and demonstrate several use cases of ASM that leverage it to provide soft performance guarantees and improve performance and fairness.

Includes members:
  Member "squash simple qos aware high performance memory scheduler for heterogeneous systems with hardware accelerators":
    also connected through other hyperedges:
      (none)
  Member "managing hybrid main memories with a page utility driven performance model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reducing dram latency at low cost by exploiting heterogeneity")

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)