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 "tlb and pagewalk performance in multicore architectures with large die stacked dram cache"):
  Abstract: In this work we study the overheads of virtual-to-physical address translation in processor architectures, like x86-64, that implement paged virtual memory using a radix tree which are walked in hardware. Translation Lookaside Buffers are critical to system performance, particularly as applications demand larger memory footprints and with the adoption of virtualization; however the cost of a TLB miss potentially results in multiple memory accesses to retrieve the translation. Architectural support for superpages has been introduced to increase TLB hits but are limited by the operating systems ability to find contiguous memory. Numerous prior studies have proposed TLB designs to lower miss rates and reduce page walk overhead; however, these studies have modeled the behavior analytically. Further, to eschew the paging overhead for big-memory workloads and virtualization, Direct Segment maps part of a process linear virtual address space with segment registers albeit requiring a few application and operating system modifications. The recently evolved die-stacked DRAM technology promises a high bandwidth and large last-level cache, in the order of Gigabytes, closer to the processors. With such large caches the amount of data that can be accessed without causing a TLB fault - the reach of a TLB, is inadequate. TLBs are on the critical path for data accesses and incurring an expensive page walk can hinder system performance, especially when the data being accessed is a cache hit in the LLC. Hence, we are interested in exploring novel address translation mechanisms, commensurate to the size and latency of stacked DRAM. By accurately simulating the multitude of multi-level address translation structures using the QEMU based MARSSx86 full system simulator, we perform detailed study of TLBs in conjunction with the large LLCs using multi-programmed and multi-threaded workloads.

Includes members:
  Member "bigdatabench a big data benchmark suite from internet services":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aibench an industry standard internet service ai benchmark suite")
  Member "hitting the memory wall implications of the obvious":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis")

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.AR(Hardware Architecture)