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 "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  Abstract: In this paper, we study how the Pruned Landmark Labeling (PPL) algorithm can be parallelized in a scalable fashion, producing the same results as the sequential algorithm. More specifically, we parallelize using a Vertex-Centric (VC) computational model on a modern SIMD powered multicore architecture. We design a new VC-PLL algorithm that resolves the apparent mismatch between the inherent sequential dependence of the PLL algorithm and the Vertex- Centric (VC) computing model. Furthermore, we introduce a novel batch execution model for VC computation and the BVC-PLL algorithm to reduce the computational inefficiency in VC-PLL. Quite surprisingly, the theoretical analysis reveals that under a reasonable assumption, BVC-PLL has lower computational and memory access costs than PLL and indicates it may run faster than PLL as a sequential algorithm. We also demonstrate how BVC-PLL algorithm can be extended to handle directed graphs and weighted graphs and how it can utilize the hierarchical parallelism on a modern parallel computing architecture. Extensive experiments on real-world graphs not only show the sequential BVC-PLL can run more than two times faster than the original PLL, but also demonstrates its parallel efficiency and scalability.

Includes members:
  Member "theoretically efficient parallel graph algorithms can be fast and scalable":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")
  Member "route planning in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles")
  Member "hop doubling label indexing for point to point distance querying on scale free networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "planting trees for scalable and efficient canonical hub labeling")
  Member "nscale neighborhood centric large scale graph analytics in the cloud":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "tree decompositions and social graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "redundancy free computation graphs for graph neural networks")
  Member "simple fast and scalable reachability oracle":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "public transit labeling")
  Member "hub accelerator fast and exact shortest path computation in large social networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quegel a general purpose query centric framework for querying big graphs")

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.DB(Databases)