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 "distributed graph clustering by load balancing"):
  Abstract: Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the past decades, researchers have proposed a number of algorithmic design methods for graph clustering. However, most of these methods are based on complicated spectral techniques or convex optimisation, and cannot be applied directly for clustering many networks that occur in practice, whose information is often collected on different sites. Designing a simple and distributed clustering algorithm is of great interest, and has wide applications for processing big datasets. In this paper we present a simple and distributed algorithm for graph clustering: for a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds, and recovers a partition of the graph close to an optimal partition. The main component of our algorithm is an application of the random matching model of load balancing, which is a fundamental protocol in distributed computing and has been extensively studied in the past 20 years. Hence, our result highlights an intrinsic and interesting connection between graph clustering and load balancing. At a technical level, we present a purely algebraic result characterising the early behaviours of load balancing processes for graphs exhibiting a cluster-structure. We believe that this result can be further applied to analyse other gossip processes, such as rumour spreading and averaging processes.

Includes members:
  Member "communication optimal distributed clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the communication complexity of optimization")
  Member "find your place simple distributed algorithms for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "partitioning well clustered graphs spectral clustering works":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "partitioning into expanders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "quasirandom load balancing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrepancy analysis of a new randomized diffusion algorithm for weighted round matrices")
  Member "approximating the expansion profile and almost optimal local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "tight bounds for randomized load balancing on arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel balanced allocations the heavily loaded case")
  Member "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)