human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a meta graph approach to analyze subgraph centric distributed programming models"
  Abstract: Component-centric distributed graph processing models that use bulk synchronous parallel (BSP) execution have grown popular. These overcome short-comings of Big Data platforms like Hadoop for processing large graphs. However, literature on formal analysis of these component-centric abstractions for different graphs, graph partitioning, and graph algorithms is lacking. Here, we propose an coarse-grained analytical approach based on a meta-graph sketch to examine the characteristics of component-centric graph programming models. We apply this sketch to subgraph- and block-centric abstractions, and draw a comparison with vertex-centric models like Google's Pregel. We explore the impact of various graph partitioning techniques on the meta-graph, and the impact of the meta-graph on graph algorithms. This decouples large unwieldy graphs and their partitioning artifacts from their algorithmic analysis. We evaluate our approach for five spatial and powerlaw graphs, four different partitioning strategies, and for PageRank and Breadth First Search algorithms. We show that this novel analytical technique is simple, scalable and yet gives a reliable estimate of the number of supersteps, and the communication and computational complexities of the algorithms for various graphs.

Connected through hyperedge HE-1 (induced by source paper "a partition centric distributed algorithm for identifying euler circuits in large graphs"):
  with members:
    - "gunrock a high performance graph processing library on the gpu"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "parallel graph partitioning for complex networks"

Question: Please tell me which class the center node 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)