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 "graphchallenge org raising the bar on graph analytic performance"):
  Abstract: The rise of graph analytic systems has created a need for new ways to measure and compare the capabilities of graph processing systems. The MIT/Amazon/IEEE Graph Challenge has been developed to provide a well-defined community venue for stimulating research and highlighting innovations in graph analysis software, hardware, algorithms, and systems. GraphChallenge.org provides a wide range of pre-parsed graph data sets, graph generators, mathematically defined graph algorithms, example serial implementations in a variety of languages, and specific metrics for measuring performance. Graph Challenge 2017 received 22 submissions by 111 authors from 36 organizations. The submissions highlighted graph analytic innovations in hardware, software, algorithms, systems, and visualization. These submissions produced many comparable performance measurements that can be used for assessing the current state of the art of the field. There were numerous submissions that implemented the triangle counting challenge and resulted in over 350 distinct measurements. Analysis of these submissions show that their execution time is a strong function of the number of edges in the graph,  $N_{e}$ , and is typically proportional to  $N_{e}^{4/3}$  for large values of  $N_{e}$ . Combining the model fits of the submissions presents a picture of the current state of the art of graph analysis, which is typically 108 edges processed per second for graphs with 108 edges. These results are 30 times faster than serial implementations commonly used by many graph analysts and underscore the importance of making these performance benefits available to the broader community. Graph Challenge provides a clear picture of current graph analysis systems and underscores the need for new innovations to achieve high performance on very large graphs.

Includes members:
  Member "streaming graph challenge stochastic block partition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "75 000 000 000 streaming inserts second using hierarchical hypersparse graphblas matrices")
  Member "distributed triangle counting in the graphulo matrix math library":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "design generation and validation of extreme scale power law graphs")
  Member "preconditioned spectral clustering for stochastic block partition streaming graph challenge preliminary version at arxiv":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "design generation and validation of extreme scale power law graphs")
  Member "an ensemble framework for detecting community changes in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "design generation and validation of extreme scale power law graphs")
  Member "scaling in internet traffic a 14 year and 3 day longitudinal study with multiscale analyses and random projections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "new phenomena in large scale internet traffic")
  Member "static graph challenge subgraph isomorphism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a systematic survey of general sparse matrix matrix multiplication")
  Member "enabling massive deep neural networks with the graphblas":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ai data wrangling with associative arrays")
  Member "pagerank pipeline benchmark proposal for a holistic system benchmark for big data platforms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse deep neural network graph challenge")

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)