human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
  Abstract: Compared to planar (i.e., two-dimensional) NAND flash memory, 3D NAND flash memory uses a new flash cell design, and vertically stacks dozens of silicon layers in a single chip. This allows 3D NAND flash memory to increase storage density using a much less aggressive manufacturing process technology than planar NAND flash memory. The circuit-level and structural changes in 3D NAND flash memory significantly alter how different error sources affect the reliability of the memory. #R##N#In this paper, through experimental characterization of real, state-of-the-art 3D NAND flash memory chips, we find that 3D NAND flash memory exhibits three new error sources that were not previously observed in planar NAND flash memory: (1) layer-to-layer process variation, where the average error rate of each 3D-stacked layer in a chip is significantly different; (2) early retention loss, a new phenomenon where the number of errors due to charge leakage increases quickly within several hours after programming; and (3) retention interference, a new phenomenon where the rate at which charge leaks from a flash cell is dependent on the data value stored in the neighboring cell. #R##N#Based on our experimental results, we develop new analytical models of layer-to-layer process variation and retention loss in 3D NAND flash memory. Motivated by our new findings and models, we develop four new techniques to mitigate process variation and early retention loss in 3D NAND flash memory. These four techniques are complementary, and can be combined together to significantly improve flash memory reliability. Compared to a state-of-the-art baseline, our techniques, when combined, improve flash memory lifetime by 1.85x. Alternatively, if a NAND flash vendor wants to keep the lifetime of the 3D NAND flash memory device constant, our techniques reduce the storage overhead required to hold error correction information by 78.9%.

Connected through hyperedge HE-1 (induced by source paper "rowhammer a retrospective"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "rowhammer and beyond"
    - "d range using commodity dram devices to generate true random numbers with low latency and high throughput"
    - "nethammer inducing rowhammer faults through network requests"
    - "pointing in the right direction securing memory accesses in a faulty world"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "theory and practice of finding eviction sets"
    - "intrinsic rowhammer pufs leveraging the rowhammer effect for improved security"

Connected through hyperedge HE-2 (induced by source paper "large scale studies of memory storage and network failures in a modern data center"):
  with members:
    - "architectural techniques for improving nand flash memory reliability"
    - "d range using commodity dram devices to generate true random numbers with low latency and high throughput"
    - "nethammer inducing rowhammer faults through network requests"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "banshee bandwidth efficient dram caching via software hardware cooperation"
    - "another flip in the wall of rowhammer defenses"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"

Connected through hyperedge HE-3 (induced by source paper "rowhammer and beyond"):
  with members:
    - "spectre attacks exploiting speculative execution"
    - "d range using commodity dram devices to generate true random numbers with low latency and high throughput"
    - "nethammer inducing rowhammer faults through network requests"
    - "another flip in the wall of rowhammer defenses"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "can t touch this practical and generic software only defenses against rowhammer attacks"

Connected through hyperedge HE-4 (induced by source paper "what your dram power models are not telling you lessons from a detailed experimental study"):
  with members:
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "understanding and improving the latency of dram based memory systems"
    - "simple dram and virtual memory abstractions to enable highly efficient memory systems"
    - "simultaneous multi layer access a high bandwidth and low cost 3d stacked memory interface"
    - "improving dram performance by parallelizing refreshes with accesses"
    - "bigdatabench a big data benchmark suite from internet services"

Connected through hyperedge HE-5 (induced by source paper "architectural techniques for improving nand flash memory reliability"):
  with members:
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "another flip in the wall of rowhammer defenses"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "using ecc dram to adaptively increase memory capacity"
    - "channel models for multi level cell flash memories based on empirical error analysis"
    - "rowhammer js a remote software induced fault attack in javascript"

Question: Please tell me which class the center node 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)