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 "architectural techniques for improving nand flash memory reliability"):
  Abstract: Raw bit errors are common in NAND flash memory and will increase in the future. These errors reduce flash reliability and limit the lifetime of a flash memory device. We aim to improve flash reliability with a multitude of low-cost architectural techniques. We show that NAND flash memory reliability can be improved at low cost and with low performance overhead by deploying various architectural techniques that are aware of higher-level application behavior and underlying flash device characteristics. #R##N#We analyze flash error characteristics and workload behavior through experimental characterization, and design new flash controller algorithms that use the insights gained from our analysis to improve flash reliability at a low cost. We investigate four directions through this approach. (1) We propose a new technique called WARM that improves flash reliability by 12.9 times by managing flash retention differently for write-hot data and write-cold data. (2) We propose a new framework that learns an online flash channel model for each chip and enables four new flash controller algorithms to improve flash reliability by up to 69.9%. (3) We identify three new error characteristics in 3D NAND through a comprehensive experimental characterization of real 3D NAND chips, and propose four new techniques that mitigate these new errors and improve 3D NAND reliability by up to 66.9%. (4) We propose a new technique called HeatWatch that improves 3D NAND reliability by 3.85 times by utilizing self-healing effect to mitigate retention errors in 3D NAND.

Includes members:
  Member "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rowhammer a retrospective")
  Member "what your dram power models are not telling you lessons from a detailed experimental study":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "another flip in the wall of rowhammer defenses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "are we susceptible to rowhammer an end to end methodology for cloud providers")
  Member "the rowhammer problem and other issues we may face as memory becomes denser":
    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")
  Member "using ecc dram to adaptively increase memory capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "processing data where it makes sense enabling in memory computation")
  Member "channel models for multi level cell flash memories based on empirical error analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "model based detector for ssds in the presence of inter cell interference")
  Member "rowhammer js a remote software induced fault attack in javascript":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)