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 and sparsification"):
  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. Most of these methods, however, are based on complicated spectral techniques or convex optimisation, and cannot be directly applied 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. #R##N#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 optimal. One of the main components behind our algorithm is a sampling scheme that, given a dense graph as input, produces a sparse subgraph that provably preserves the cluster-structure of the input. Compared with previous sparsification algorithms that require Laplacian solvers or involve combinatorial constructions, this component is easy to implement in a distributed way and runs fast in practice.

Includes members:
  Member "an sdp based algorithm for linear sized spectral sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a spectral approach to network design")
  Member "communication optimal distributed clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the communication complexity of optimization")
  Member "average whenever you meet opportunistic protocols for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "find your place simple distributed algorithms for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "constructing linear sized spectral sparsification in almost linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a spectral approach to network design")
  Member "partitioning well clustered graphs spectral clustering works":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "partitioning into expanders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient algorithms for the potts model on small set expanders")
  Member "approximating the expansion profile and almost optimal local graph clustering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient algorithms for the potts model on small set expanders")

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)