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 "graphmp i o efficient big graph analytics on a single commodity machine"):
  Abstract: Recent studies showed that single-machine graph processing systems can be as highly competitive as cluster-based approaches on large-scale problems. While several out-of-core graph processing systems and computation models have been proposed, the high disk I/O overhead could significantly reduce performance in many practical cases. In this paper, we propose GraphMP to tackle big graph analytics on a single machine. GraphMP achieves low disk I/O overhead with three techniques. First, we design a vertex-centric sliding window (VSW) computation model to avoid reading and writing vertices on disk. Second, we propose a selective scheduling method to skip loading and processing unnecessary edge shards on disk. Third, we use a compressed edge cache mechanism to fully utilize the available memory of a machine to reduce the amount of disk accesses for edges. Extensive evaluations have shown that GraphMP could outperform existing single-machine out-of-core systems such as GraphChi, X-Stream and GridGraph by up to 51, and can be as highly competitive as distributed graph engines like Pregel+, PowerGraph and Chaos.

Includes members:
  Member "high level programming abstractions for distributed graph processing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "graphh high performance big graph analytics in small clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "graphmp an efficient semi external memory big graph processing system on a single machine")
  Member "graphmp an efficient semi external memory big graph processing system on a single machine":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi asymmetric parallel graph algorithms for nvrams")
  Member "gunrock a high performance graph processing library on the gpu":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hygcn a gcn accelerator with hybrid architecture")
  Member "effective techniques for message reduction and load balancing in distributed graph computation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "flashgraph processing billion node graphs on an array of commodity ssds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")
  Member "pregelix big ger graph analytics on a dataflow engine":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graph3s a simple speedy and scalable distributed graph processing system")

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)