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: "graphcage cache aware graph processing on gpus"
  Abstract: Efficient Graph processing is challenging because of the irregularity of graph algorithms. Using GPUs to accelerate irregular graph algorithms is even more difficult to be efficient, since GPU's highly structured SIMT architecture is not a natural fit for irregular applications. With lots of previous efforts spent on subtly mapping graph algorithms onto the GPU, the performance of graph processing on GPUs is still highly memory-latency bound, leading to low utilization of compute resources. Random memory accesses generated by the sparse graph data structure are the major causes of this significant memory access latency. Simply applying the conventional cache blocking technique proposed for matrix computation have limited benefit due to the significant overhead on the GPU. We propose GraphCage, a cache centric optimization framework for highly efficient graph processing on GPUs. We first present a throughput-oriented cache blocking scheme (TOCAB) in both push and pull directions. Comparing with conventional cache blocking which suffers repeated accesses when processing large graphs on GPUs, TOCAB is specifically optimized for the GPU architecture to reduce this overhead and improve memory access efficiency. To integrate our scheme into state-of-the-art implementations without significant overhead, we coordinate TOCAB with load balancing strategies by considering the sparsity of subgraphs. To enable cache blocking for traversal-based algorithms, we consider the benefit and overhead in different iterations with different working set sizes, and apply TOCAB for topology-driven kernels in pull direction. Evaluation shows that GraphCage can improve performance by 2 ~ 4x compared to hand optimized implementations and state-of-the-art frameworks (e.g. CuSha and Gunrock), with less memory consumption than CuSha.

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.DC(Distributed, Parallel, and Cluster Computing)