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: "what your dram power models are not telling you lessons from a detailed experimental study"
  Abstract: Main memory (DRAM) consumes as much as half of the total system power in a computer today, resulting in a growing need to develop new DRAM architectures and systems that consume less power. Researchers have long relied on DRAM power models that are based off of standardized current measurements provided by vendors, called IDD values. Unfortunately, we find that these models are highly inaccurate, and do not reflect the actual power consumed by real DRAM devices. #R##N#We perform the first comprehensive experimental characterization of the power consumed by modern real-world DRAM modules. Our extensive characterization of 50 DDR3L DRAM modules from three major vendors yields four key new observations about DRAM power consumption: (1) across all IDD values that we measure, the current consumed by real DRAM modules varies significantly from the current specified by the vendors; (2) DRAM power consumption strongly depends on the data value that is read or written; (3) there is significant structural variation, where the same banks and rows across multiple DRAM modules from the same model consume more power than other banks or rows; and (4) over successive process technology generations, DRAM power consumption has not decreased by as much as vendor specifications have indicated. #R##N#Based on our detailed analysis and characterization data, we develop the Variation-Aware model of Memory Power Informed by Real Experiments (VAMPIRE). We show that VAMPIRE has a mean absolute percentage error of only 6.8% compared to actual measured DRAM power. VAMPIRE enables a wide range of studies that were not possible using prior DRAM power models. As an example, we use VAMPIRE to evaluate a new power-aware data encoding mechanism, which can reduce DRAM energy consumption by an average of 12.2%. We plan to open-source both VAMPIRE and our extensive raw data collected during our experimental characterization.

Connected through hyperedge HE-1 (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"
    - "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"
    - "lazypim efficient support for cache coherence in processing in memory architectures"

Connected through hyperedge HE-2 (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"
    - "theory and practice of finding eviction sets"
    - "intrinsic rowhammer pufs leveraging the rowhammer effect for improved security"

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"
    - "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-4 (induced by source paper "a workload and programming ease driven perspective of processing in memory"):
  with members:
    - "processing data where it makes sense enabling in memory computation"
    - "in dram bulk bitwise execution engine"
    - "rowhammer a retrospective"
    - "accelerating bulk bit wise x n or operation in processing in dram platform"
    - "shouji a fast and efficient pre alignment filter for sequence alignment"
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "memristive devices for computation in memory"
    - "the rowhammer problem and other issues we may face as memory becomes denser"

Connected through hyperedge HE-5 (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"
    - "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"
    - "improving dram performance by parallelizing refreshes with accesses"

Connected through hyperedge HE-6 (induced by source paper "analysis of dawnbench a time to accuracy machine learning performance benchmark"):
  with members:
    - "beyond data and model parallelism for deep neural networks"
    - "nvidia tensor core programmability performance precision"
    - "an empirical model of large batch training"
    - "regularized evolution for image classifier architecture search"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "exploring the limits of weakly supervised pretraining"
    - "revisiting small batch training for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "architectural techniques for improving nand flash memory reliability"):
  with members:
    - "improving 3d nand flash memory lifetime by tolerating early retention loss and process variation"
    - "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"

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"
    - "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)