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 "shared memory branch and reduce for multiterminal cuts"):
  Abstract: We introduce the fastest known exact algorithm~for~the multiterminal cut problem with k terminals. In particular, we engineer existing as well as new data reduction rules. We use the rules within a branch-and-reduce framework and to boost the performance of an ILP formulation. Our algorithms achieve improvements in running time of up to multiple orders of magnitudes over the ILP formulation without data reductions, which has been the de facto standard used by practitioners. This allows us to solve instances to optimality that are significantly larger than was previously possible.

Includes members:
  Member "ilp based local search for graph partitioning":
    also connected through other hyperedges:
      (none)
  Member "practical minimum cut algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice")
  Member "shared memory exact minimum cuts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding all global minimum cuts in practice")
  Member "communication free massively distributed graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances in scalable network generation")
  Member "local flow partitioning for faster edge connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved algorithm for min cuts in distributed networks")
  Member "an o 1 84 k parameterized algorithm for the multiterminal cut problem":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "edge bipartization faster than 2 k")
  Member "empirical comparison of algorithms for network community detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)