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 "practical minimum cut algorithms"):
  Abstract: The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges. Here, we introduce a linear-time algorithm to compute near-minimum cuts. Our algorithm is based on cluster contraction using label propagation and Padberg and Rinaldi’s contraction heuristics [SIAM Review, 1991]. We give both sequential and shared-memory parallel implementations of our algorithm. Extensive experiments on both real-world and generated instances show that our algorithm finds the optimal cut on nearly all instances significantly faster than other state-of-the-art exact algorithms, and our error rate is lower than that of other heuristic algorithms. In addition, our parallel algorithm runs a factor 7.5× faster on average when using 32 threads. To further speed up computations, we also give a version of our algorithm that performs random edge contractions as preprocessing. This version achieves a lower running time and better parallel scalability at the expense of a higher error rate.

Includes members:
  Member "local flow partitioning for faster edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improved algorithm for min cuts in distributed networks")
  Member "concurrent hash tables fast and general":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding all global minimum cuts in practice")
  Member "deterministic global minimum cut of a simple graph in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved algorithm for min cuts in distributed networks")
  Member "networkit an interactive tool suite for high performance network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "semi external algorithms for graph partitioning and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable graph algorithms")
  Member "on the analysis of a label propagation algorithm for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient distributed community detection in the stochastic block model")
  Member "layered label propagation a multiresolution coordinate free ordering for compressing social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "2ps high quality edge partitioning with two phase streaming")
  Member "minimum cuts in near linear time":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "approximating spectral clustering via sampling a review")

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)