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: "tiered latency dram enabling low latency main memory at low cost"
  Abstract: This paper summarizes the idea of Tiered-Latency DRAM (TL-DRAM), which was published in HPCA 2013, and examines the work's significance and future potential. The capacity and cost-per-bit of DRAM have historically scaled to satisfy the needs of increasingly large and complex computer systems. However, DRAM latency has remained almost constant, making memory latency the performance bottleneck in today's systems. We observe that the high access latency is not intrinsic to DRAM, but a trade-off is made to decrease the cost per bit. To mitigate the high area overhead of DRAM sensing structures, commodity DRAMs connect many DRAM cells to each sense amplifier through a wire called a bitline. These bit-lines have a high parasitic capacitance due to their long length, and this bitline capacitance is the dominant source of DRAM latency. Specialized low-latency DRAMs use shorter bitlines with fewer cells, but have a higher cost-per-bit due to greater sense amplifier area overhead. To achieve both low latency and low cost per bit, we introduce Tiered-Latency DRAM (TL-DRAM). In TL-DRAM, each long bitline is split into two shorter segments by an isolation transistor, allowing one of the two segments to be accessed with the latency of a short-bitline DRAM without incurring a high cost per bit. We propose mechanisms that use the low-latency segment as a hardware-managed or software-managed cache. Our evaluations show that our proposed mechanisms improve both performance and energy efficiency for both single-core and multiprogrammed workloads. Tiered-Latency DRAM has inspired several other works on reducing DRAM latency with little to no architectural modification.

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)