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 dram performance by parallelizing refreshes with accesses"
  Abstract: Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR (double data rate) DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire DRAM rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR (low power DDR) DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. Unfortunately, there are two shortcomings of per-bank refresh employed in today's systems. First, we observe that the perbank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, as it is done today, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP proactively schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations on a wide variety of workloads and systems show that our mechanisms improve system performance (and energy efficiency) compared to three state-of-the-art refresh policies and the performance benefit increases as DRAM density increases.

Connected through hyperedge HE-1 (induced by source paper "are we susceptible to rowhammer an end to end methodology for cloud providers"):
  with members:
    - "rowhammer a retrospective"
    - "spoiler speculative load hazards boost rowhammer and cache attacks"
    - "nethammer inducing rowhammer faults through network requests"
    - "pointing in the right direction securing memory accesses in a faulty world"
    - "another flip in the wall of rowhammer defenses"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "can t touch this practical and generic software only defenses against rowhammer attacks"
    - "rowhammer js a remote software induced fault attack in javascript"

Connected through hyperedge HE-2 (induced by source paper "eden enabling energy efficient high performance deep neural network inference using approximate dram"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "in dram bulk bitwise execution engine"
    - "analyzing and mitigating the impact of permanent faults on a systolic array based neural network accelerator"
    - "amc automl for model compression and acceleration on mobile devices"
    - "high accuracy low precision training"
    - "nullanet training deep neural networks for reduced memory access inference"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "tvm an automated end to end optimizing compiler for deep learning"

Connected through hyperedge HE-3 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "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"
    - "using ecc dram to adaptively increase memory capacity"

Connected through hyperedge HE-4 (induced by source paper "rowhammer a retrospective"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "rowhammer and beyond"
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "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"

Connected through hyperedge HE-5 (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"
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "rowhammer and beyond"):
  with members:
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "in dram bulk bitwise execution engine"):
  with members:
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "d range using commodity dram devices to generate true random numbers with low latency and high throughput"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "neural cache bit serial in cache acceleration of deep neural networks"
    - "understanding reduced voltage operation in modern dram chips characterization analysis and mechanisms"
    - "reducing dram latency at low cost by exploiting heterogeneity"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "simple dram and virtual memory abstractions to enable highly efficient memory systems"

Connected through hyperedge HE-8 (induced by source paper "understanding the interactions of workloads and dram types a comprehensive experimental study"):
  with members:
    - "in dram bulk bitwise execution engine"
    - "rowhammer a retrospective"
    - "memory centric characterization and analysis of spec cpu2017 suite"
    - "d range using commodity dram devices to generate true random numbers with low latency and high throughput"
    - "understanding and improving the latency of dram based memory systems"
    - "reducing dram latency at low cost by exploiting heterogeneity"

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)