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 "recent advances in overcoming bottlenecks in memory systems and managing memory resources in gpu systems"):
  Abstract: This article features extended summaries and retrospectives of some of the recent research done by our research group, SAFARI, on (1) various critical problems in memory systems and (2) how memory system bottlenecks affect graphics processing unit (GPU) systems. As more applications share a single system, operations from each application can contend with each other at various shared components. Such contention can slow down each application or thread of execution. The compound effect of contention, high memory latency and access overheads, as well as inefficient management of resources, greatly degrades performance, quality-of-service, and energy efficiency. The ten works featured in this issue study several aspects of (1) inter-application interference in multicore systems, heterogeneous systems, and GPUs; (2) the growing overheads and expenses associated with growing memory densities and latencies; and (3) performance, programmability, and portability issues in modern GPUs, especially those related to memory system resources.

Includes members:
  Member "errors in flash memory based solid state drives analysis mitigation and recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "error characterization mitigation and recovery in flash memory based solid state drives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "understanding and improving the latency of dram based memory systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram")
  Member "improving dram performance by parallelizing refreshes with accesses":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "are we susceptible to rowhammer an end to end methodology for cloud providers")

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)