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: "understanding and improving the latency of dram based memory systems"
  Abstract: Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory access. #R##N#In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. #R##N#The key conclusion of this dissertation is that augmenting DRAM architecture with simple and low-cost features, and developing a better understanding of manufactured DRAM chips together lead to significant memory latency reduction as well as energy efficiency improvement. We hope and believe that the proposed architectural techniques and the detailed experimental data and observations on real commodity DRAM chips presented in this dissertation will enable development of other new mechanisms to improve the performance, energy efficiency, or reliability of future memory systems.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (induced by source paper "enabling practical processing in and near memory for data intensive computing"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "rowhammer a retrospective"
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "using ecc dram to adaptively increase memory capacity"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"

Connected through hyperedge HE-5 (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"
    - "reducing dram latency at low cost by exploiting heterogeneity"
    - "improving dram performance by parallelizing refreshes with accesses"

Connected through hyperedge HE-6 (induced by source paper "enabling the adoption of processing in memory challenges mechanisms future research directions"):
  with members:
    - "using ecc dram to adaptively increase memory capacity"
    - "lazypim efficient support for cache coherence in processing in memory architectures"
    - "reducing dram latency at low cost by exploiting heterogeneity"
    - "rowhammer reliability analysis and security implications"
    - "buddy ram improving the performance and efficiency of bulk bitwise operations using dram"
    - "the processing using memory paradigm in dram bulk copy initialization bitwise and and or"
    - "simple dram and virtual memory abstractions to enable highly efficient memory systems"
    - "improving dram performance by parallelizing refreshes with accesses"

Connected through hyperedge HE-7 (induced by source paper "what your dram power models are not telling you lessons from a detailed experimental study"):
  with members:
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "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"
    - "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-8 (induced by source paper "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"):
  with members:
    - "read disturb errors in mlc nand flash memory"
    - "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"

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)