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: "to update or not to update bandwidth efficient intelligent replacement policies for dram caches"
  Abstract: This paper investigates intelligent replacement policies for improving the hit-rate of gigascale DRAM caches. Cache replacement policies are commonly used to improve the hit-rate of on-chip caches. The most effective replacement policies often require the cache to track per-line reuse state to inform their decision. A fundamental challenge on DRAM caches, however, is that stateful policies would require significant bandwidth to maintain per-line DRAM cache state. As such, DRAM cache replacement policies have primarily been stateless policies, such as always-install or probabilistic bypass. Unfortunately, we find that stateless policies are often too coarse-grain and become ineffective at the size and associativity of DRAM caches. Ideally, we want a replacement policy that can obtain the hit-rate benefits of stateful replacement policies, but keep the bandwidth-efficiency of stateless policies. #R##N#In our study, we find that tracking per-line reuse state can enable an effective replacement policy that can mitigate common thrashing patterns seen in gigascale caches. We propose a stateful replacement/bypass policy called RRIP Age-On-Bypass (RRIP-AOB), that tracks reuse state for high-reuse lines, protects such lines by bypassing other lines, and Ages the state On cache Bypass. Unfortunately, such a stateful technique requires significant bandwidth to update state. To this end, we propose Efficient Tracking of Reuse (ETR). ETR makes state tracking efficient by accurately tracking the state of only one line from a region, and using the state of that line to guide the replacement decisions for other lines in that region. ETR reduces the bandwidth for tracking replacement state by 70%, and makes stateful policies practical for DRAM caches. Our evaluations with a 2GB DRAM cache, show that our RRIP-AOB and ETR techniques provide 18% speedup while needing less than 1KB of SRAM.

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)