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: "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.

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"
    - "shared memory exact minimum cuts"

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"
    - "scalable edge partitioning"
    - "shared memory exact minimum cuts"
    - "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 "a simple algorithm for minimum cuts in near linear time"):
  with members:
    - "distributed edge connectivity in sublinear time"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "parallel minimum cuts in near linear work and low depth"
    - "local flow partitioning for faster edge connectivity"
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "deterministic edge connectivity in near linear time"
    - "distributed minimum cut approximation"
    - "maintaining information in fully dynamic trees with top trees"

Connected through hyperedge HE-4 (induced by source paper "shared memory branch and reduce for multiterminal cuts"):
  with members:
    - "ilp based local search for graph partitioning"
    - "shared memory exact minimum cuts"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "shared memory exact minimum cuts"):
  with members:
    - "local flow partitioning for faster edge connectivity"
    - "concurrent hash tables fast and general"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "networkit an interactive tool suite for high performance network analysis"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "an o m algorithm for cores decomposition of networks"

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)