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: "read disturb errors in mlc nand flash memory"
  Abstract: This paper summarizes our work on experimentally characterizing, mitigating, and recovering read disturb errors in multi-level cell (MLC) NAND flash memory, which was published in DSN 2015, and examines the work's significance and future potential. NAND flash memory reliability continues to degrade as the memory is scaled down and more bits are programmed per cell. A key contributor to this reduced reliability is read disturb, where a read to one row of cells impacts the threshold voltages of unread flash cells in different rows of the same block. #R##N#For the first time in open literature, this work experimentally characterizes read disturb errors on state-of-the-art 2Y-nm (i.e., 20-24 nm) MLC NAND flash memory chips. Our findings (1) correlate the magnitude of threshold voltage shifts with read operation counts, (2) demonstrate how program/erase cycle count and retention age affect the read-disturb-induced error rate, and (3) identify that lowering pass-through voltage levels reduces the impact of read disturb and extend flash lifetime. Particularly, we find that the probability of read disturb errors increases with both higher wear-out and higher pass-through voltage levels. #R##N#We leverage these findings to develop two new techniques. The first technique mitigates read disturb errors by dynamically tuning the pass-through voltage on a per-block basis. Using real workload traces, our evaluations show that this technique increases flash memory endurance by an average of 21%. The second technique recovers from previously-uncorrectable flash errors by identifying and probabilistically correcting cells susceptible to read disturb errors. Our evaluations show that this recovery technique reduces the raw bit error rate by 36%.

Connected through hyperedge HE-1 (induced by source paper "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"):
  with members:
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "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"
    - "understanding and improving the latency of dram based memory systems"

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)