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 "uncertain graph sparsification"):
  Abstract: Uncertain graphs are prevalent in several applications including communications systems, biological databases and social networks. The ever increasing size of the underlying data renders both graph storage and query processing extremely expensive. Sparsification has often been used to reduce the size of deterministic graphs by maintaining only the important edges. However, adaptation of deterministic sparsification methods fails in the uncertain setting. To overcome this problem, we introduce the first sparsification techniques aimed explicitly at uncertain graphs. The proposed methods reduce the number of edges and redistribute their probabilities in order to decrease the graph size, while preserving its underlying structure. The resulting graph can be used to efficiently and accurately approximate any query and mining tasks on the original graph. An extensive experimental evaluation with real and synthetic datasets illustrates the effectiveness of our techniques on several common graph tasks, including clustering coefficient, page rank, reliability and shortest path distance.

Includes members:
  Member "on fully dynamic graph sparsifiers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "structure preserving sparsification of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "single and multi level network sparsification by algebraic distance")
  Member "simple parallel and distributed algorithms for spectral graph sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "single pass spectral sparsification in dynamic streams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "mining maximal cliques from an uncertain graph":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the enumeration of maximal delta gamma cliques of a temporal network")
  Member "improved parallel algorithms for spanners and hopsets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")
  Member "distance preserving graph simplification":
    also connected through other hyperedges:
      (none)
  Member "randomized approximation schemes for cuts and flows in capacitated graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")

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)