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: "computing minimum cuts in hypergraphs"
  Abstract: We study algorithmic and structural aspects of connectivity in hypergraphs. Given a hypergraph $H=(V,E)$ with $n = |V|$, $m = |E|$ and $p = \sum_{e \in E} |e|$ the best known algorithm to compute a global minimum cut in $H$ runs in time $O(np)$ for the uncapacitated case and in $O(np + n^2 \log n)$ time for the capacitated case. We show the following new results. #R##N#1. Given an uncapacitated hypergraph $H$ and an integer $k$ we describe an algorithm that runs in $O(p)$ time to find a subhypergraph $H'$ with sum of degrees $O(kn)$ that preserves all edge-connectivities up to $k$ (a $k$-sparsifier). This generalizes the corresponding result of Nagamochi and Ibaraki from graphs to hypergraphs. Using this sparsification we obtain an $O(p + \lambda n^2)$ time algorithm for computing a global minimum cut of $H$ where $\lambda$ is the minimum cut value. #R##N#2. We generalize Matula's argument for graphs to hypergraphs and obtain a $(2+\epsilon)$-approximation to the global minimum cut in a capacitated hypergraph in $O(\frac{1}{\epsilon} (p \log n + n \log^2 n))$ time. #R##N#3. We show that a hypercactus representation of all the global minimum cuts of a capacitated hypergraph can be computed in $O(np + n^2 \log n)$ time and $O(p)$ space. #R##N#We utilize vertex ordering based ideas to obtain our results. Unlike graphs we observe that there are several different orderings for hypergraphs which yield different insights.

Connected through hyperedge HE-1 (induced by source paper "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"):
  with members:
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "quadratic decomposable submodular function minimization"
    - "submodularity on hypergraphs from sets to sequences"
    - "inhomogoenous hypergraph clustering with applications"
    - "cheeger inequalities for submodular transformations"
    - "higher order organization of complex networks"

Connected through hyperedge HE-2 (induced by source paper "revisiting decomposable submodular function minimization with incidence relations"):
  with members:
    - "hypergcn a new method of training graph convolutional networks on hypergraphs"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "inhomogoenous hypergraph clustering with applications"
    - "semi supervised classification with graph convolutional networks"
    - "random coordinate descent methods for minimizing decomposable submodular functions"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "scalable variational inference in log supermodular models"

Connected through hyperedge HE-3 (induced by source paper "a note on approximate strengths of edges in a hypergraph"):
  with members:
    - "sketching cuts in graphs and hypergraphs"

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)