human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "accelerating pagerank using partition centric processing"):
  Abstract: PageRank is a fundamental link analysis algorithm and a key representative of the performance of other graph algorithms and Sparse Matrix Vector (SpMV) multiplication. Calculating PageRank on sparse graphs generates large amount of random memory accesses resulting in low cache line utilization and poor use of memory bandwidth. In this paper, we present a novel Partition-Centric Processing Methodology (PCPM) that drastically reduces the amount of communication with DRAM and achieves high memory bandwidth. Similar to the state of the art Binning with Vertex-centric Gather-Apply-Scatter (BVGAS) method, PCPM performs partition wise scatter and gather of updates with both phases enjoying full cache line utilization. However, BVGAS suffers from random memory accesses and redundant read/write of update values from nodes to their neighbors. In contrast, PCPM propagates single update from source node to all destinations in a partition, thus decreasing redundancy effectively. We make use of this characteristic to develop a novel bipartite Partition-Node Graph (PNG) data layout for PCPM, that enables streaming memory accesses, with very little generation overhead. We perform detailed analysis of PCPM and provide theoretical bounds on the amount of communication and random DRAM accesses. We experimentally evaluate our approach using 6 large graph datasets and demonstrate an average 2.7x speedup in execution time and 1.7x reduction in communication, compared to the state of the art. We also show that unlike the BVGAS implementation, PCPM is able to take advantage of intelligent node labeling that enhances locality in graphs, by further reducing the amount of communication with DRAM. Although we use PageRank as the target application in this paper, our approach can be applied to generic SpMV computation.

Includes members:
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")
  Member "branch avoiding graph algorithms":
    also connected through other hyperedges:
      (none)
  Member "evolution of social attribute networks measurements modeling and implications using google":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "defending against machine learning based inference attacks via adversarial examples opportunities and challenges")
  Member "layered label propagation a multiresolution coordinate free ordering for compressing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "2ps high quality edge partitioning with two phase streaming")
  Member "hitting the memory wall implications of the obvious":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis")

Question: Please tell me which class the source paper 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)