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 "theoretically efficient parallel graph algorithms can be fast and scalable"):
  Abstract: There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 13 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We provide a publicly-available benchmark suite containing our implementations.

Includes members:
  Member "single source bottleneck path algorithm faster than sorting for sparse graphs":
    also connected through other hyperedges:
      (none)
  Member "local algorithms for hierarchical dense subgraph discovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs")
  Member "parallelism in randomized incremental algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "bigsparse high performance external graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "single machine graph analytics on massive datasets using intel optane dc persistent memory")
  Member "implicit decomposition for write efficient connectivity algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams")
  Member "nearly work efficient parallel algorithm for digraph reachability":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "faster parallel algorithm for approximate shortest path")
  Member "a new efficient construction on probabilistic tree embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "covering metric spaces by few trees")
  Member "compressing graphs and indexes with recursive graph bisection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "techniques for inverted index compression")

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.DS(Data Structures and Algorithms)