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: "fast and deterministic approximations for k cut"
  Abstract: In an undirected graph, a $k$-cut is a set of edges whose removal breaks the graph into at least $k$ connected components. The minimum weight $k$-cut can be computed in $O(n^{O(k)})$ time, but when $k$ is treated as part of the input, computing the minimum weight $k$-cut is NP-Hard [Holdschmidt and Hochbaum 1994]. For $\operatorname{poly}(m,n,k)$-time algorithms, the best possible approximation factor is essentially 2 under the small set expansion hypothesis [Manurangsi 2017]. Saran and Vazirani [1995] showed that a $(2 - 2/k)$-approximately minimum weight $k$-cut can be computed by $O(k)$ minimum cuts, which implies an $\tilde{O}(mk)$ randomized running time via the nearly linear time randomized min-cut algorithm of Karger [2000]. Nagamochi and Kamidoi [2007] showed that the minimum weight $k$-cut can be computed deterministically in $O(mn + n^2 \log n)$ time. These results prompt two basic questions. The first concerns the role of randomization. Is there a deterministic algorithm for 2-approximate $k$-cuts matching the randomized running time of $\tilde{O}(mk)$? The second question qualitatively compares minimum cut to 2-approximate minimum $k$-cut. Can 2-approximate $k$-cuts be computed as fast as the (exact) minimum cut - in $\tilde{O}(m)$ randomized time? #R##N#We make progress on these questions with a deterministic approximation algorithm that computes $(2 + \epsilon)$-minimum $k$-cuts in $O(m \log^3(n) / \epsilon^2)$ time, via a $(1 + \epsilon)$-approximate for an LP relaxation of $k$-cut.

Connected through hyperedge HE-1 (induced by source paper "the number of minimum k cuts improving the karger stein bound"):
  with members:
    - "lp relaxation and tree packing for minimum k cuts"
    - "faster exact and approximate algorithms for k cut"
    - "an fpt algorithm beating 2 approximation for k cut"
    - "if the current clique algorithms are optimal so is valiant s parser"
    - "powers of tensors and fast matrix multiplication"
    - "designing fpt algorithms for cut problems using randomized contractions"
    - "minimum k way cut of bounded size is fixed parameter tractable"
    - "tight approximation ratio of a general greedy splitting algorithm for the minimum k way cut problem"

Connected through hyperedge HE-2 (induced by source paper "lp relaxation and tree packing for minimum k cuts"):
  with members:
    - "faster exact and approximate algorithms for k cut"
    - "deterministic global minimum cut of a simple graph in near linear time"
    - "minimum cuts in near linear time"

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)