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: "holistic management of the gpgpu memory hierarchy to manage warp level latency tolerance"
  Abstract: In a modern GPU architecture, all threads within a warp execute the same instruction in lockstep. For a memory instruction, this can lead to memory divergence: the memory requests for some threads are serviced early, while the remaining requests incur long latencies. This divergence stalls the warp, as it cannot execute the next instruction until all requests from the current instruction complete. In this work, we make three new observations. First, GPGPU warps exhibit heterogeneous memory divergence behavior at the shared cache: some warps have most of their requests hit in the cache, while other warps see most of their request miss. Second, a warp retains the same divergence behavior for long periods of execution. Third, requests going to the shared cache can incur queuing delays as large as hundreds of cycles, exacerbating the effects of memory divergence. We propose a set of techniques, collectively called Memory Divergence Correction (MeDiC), that reduce the negative performance impact of memory divergence and cache queuing. MeDiC delivers an average speedup of 21.8%, and 20.1% higher energy efficiency, over a state-of-the-art GPU cache management mechanism across 15 different GPGPU applications.

Connected through hyperedge HE-1 (induced by source paper "instructions latencies characterization for nvidia gpgpus"):
  with members:
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "mixed precision training"
    - "dissecting gpu memory hierarchy through microbenchmarking"

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)