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 "gunrock gpu graph analytics"):
  Abstract: For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs, have presented two significant challenges to developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We characterize the performance of various optimization strategies and evaluate Gunrock's overall performance on different GPU architectures on a wide range of graph primitives that span from traversal-based algorithms and ranking algorithms, to triangle counting and bipartite-graph-based algorithms. The results show that on a single GPU, Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives and CPU shared-memory graph libraries such as Ligra and Galois, and better performance than any other GPU high-level graph library.

Includes members:
  Member "a comparative study on exact triangle counting algorithms on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "snap a general purpose network analysis and graph mining library":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive low level storage of very large knowledge graphs")
  Member "counting triangles in large graphs on gpu":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "mathematical foundations of the graphblas":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "accelerating direction optimized breadth first search on hybrid architectures":
    also connected through other hyperedges:
      (none)
  Member "multi gpu graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an adaptive load balancer for graph analytical applications on gpus")
  Member "emptyheaded boolean algebra based graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "survey and taxonomy of lossless graph compression and space efficient graph representations")
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")

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)