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 "streaming graph partitioning in the planted partition model"):
  Abstract: The sheer increase in the size of graph data has created a lot of interest into developing efficient distributed graph processing frameworks. Popular existing frameworks such as Graphlab and Pregel rely on balanced graph partitioning in order to minimize communication and achieve work balance. #R##N#In this work we contribute to the recent research line of streaming graph partitioning \cite{stantonstreaming,stanton,fennel} which computes an approximately balanced $k$-partitioning of the vertex set of a graph using a single pass over the graph stream using degree-based criteria. This graph partitioning framework is well tailored to processing large-scale and dynamic graphs. In this work we introduce the use of higher length walks for streaming graph partitioning and show that their use incurs a minor computational cost which can significantly improve the quality of the graph partition. We perform an average case analysis of our algorithm using the planted partition model \cite{condon2001algorithms,mcsherry2001spectral}. We complement the recent results of Stanton \cite{stantonstreaming} by showing that our proposed method recovers the true partition with high probability even when the gap of the model tends to zero as the size of the graph grows. Furthermore, among the wide number of choices for the length of the walks we show that the proposed length is optimal. Finally, we conduct experiments which verify the value of the proposed method.

Includes members:
  Member "recent advances in graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multilevel acyclic hypergraph partitioning")
  Member "graphlab a new framework for parallel machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relaxed scheduling for scalable belief propagation")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive sensing for cut improvement and local clustering")

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)