human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "shared memory exact minimum cuts"
  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. In this paper, we engineer the fastest known exact algorithm for the problem. #R##N#State-of-the-art algorithms like the algorithm of Padberg and Rinaldi or the algorithm of Nagamochi, Ono and Ibaraki identify edges that can be contracted to reduce the graph size such that at least one minimum cut is maintained in the contracted graph. Our algorithm achieves improvements in running time over these algorithms by a multitude of techniques. First, we use a recently developed fast and parallel \emph{inexact} minimum cut algorithm to obtain a better bound for the problem. Then we use reductions that depend on this bound, to reduce the size of the graph much faster than previously possible. We use improved data structures to further improve the running time of our algorithm. Additionally, we parallelize the contraction routines of Nagamochi, Ono and Ibaraki. Overall, we arrive at a system that outperforms the fastest state-of-the-art solvers for the \emph{exact} minimum cut problem significantly.

Connected through hyperedge HE-1 (induced by source paper "finding all global minimum cuts in practice"):
  with members:
    - "engineering kernelization for maximum cut"
    - "shared memory branch and reduce for multiterminal cuts"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "wegotyoucovered the winning solver from the pace 2019 implementation challenge vertex cover track"
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "experimental evaluation of parameterized algorithms for feedback vertex set"
    - "practical access to dynamic programming on tree decompositions"
    - "practical minimum cut algorithms"

Connected through hyperedge HE-2 (induced by source paper "scalable graph algorithms"):
  with members:
    - "exactly solving the maximum weight independent set problem on large real world graphs"
    - "memetic graph clustering"
    - "practical minimum cut algorithms"
    - "scalable edge partitioning"
    - "scalable kernelization for maximum independent sets"
    - "graph partitioning with acyclicity constraints"
    - "memetic multilevel hypergraph partitioning"
    - "communication free massively distributed graph generation"

Connected through hyperedge HE-3 (induced by source paper "shared memory branch and reduce for multiterminal cuts"):
  with members:
    - "ilp based local search for graph partitioning"
    - "practical minimum cut algorithms"
    - "communication free massively distributed graph generation"
    - "local flow partitioning for faster edge connectivity"
    - "an o 1 84 k parameterized algorithm for the multiterminal cut problem"
    - "empirical comparison of algorithms for network community detection"

Question: Please tell me which class the center node 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)