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: "union of random minkowski sums and network vulnerability analysis"
  Abstract: Let $\mathcal{C}=\{C_1,\ldots,C_n\}$ be a set of $n$ pairwise-disjoint convex sets of constant description complexity, and let $\pi$ be a probability density function (pdf for short) over the non-negative reals. For each $i$, let $K_i$ be the Minkowski sum of $C_i$ with a disk of radius $r_i$, where each $r_i$ is a random non-negative number drawn independently from the distribution determined by $\pi$. We show that the expected complexity of the union of $K_1, \ldots, K_n$ is $O(n^{1+\varepsilon})$ for any $\varepsilon > 0$; here the constant of proportionality depends on $\varepsilon$ and on the description complexity of the sets in $\mathcal{C}$, but not on $\pi$. If each $C_i$ is a convex polygon with at most $s$ vertices, then we show that the expected complexity of the union is $O(s^2n\log n)$. #R##N#Our bounds hold in the stronger model in which we are given an arbitrary multi-set $R=\{r_1,\ldots,r_n\}$ of expansion radii, each a non-negative real number. We assign them to the members of $\mathcal{C}$ by a random permutation, where all permutations are equally likely to be chosen; the expectations are now with respect to these permutations. #R##N#We also present an application of our results to a problem that arises in analyzing the vulnerability of a network to a physical attack. %

Connected through hyperedge HE-1 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "estimating continuous distributions in bayesian classifiers"
    - "geometric inference on kernel density estimates"

Connected through hyperedge HE-2 (induced by source paper "minimum shared power edge cut"):
  with members:
    - "on the complexity of barrier resilience for fat regions"

Connected through hyperedge HE-3 (induced by source paper "the number of holes in the union of translates of a convex set in three dimensions"):
  with members:
    - "improved approximation algorithms for geometric set cover"
    - "computational geometry column 42"

Connected through hyperedge HE-4 (induced by source paper "from proximity to utility a voronoi partition of pareto optima"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "on the complexity of randomly weighted voronoi diagrams"
    - "threshold phenomena in k dominant skylines of random samples"

Connected through hyperedge HE-5 (induced by source paper "on the complexity of randomly weighted voronoi diagrams"):
  with members:
    - "sharp bounds on davenport schinzel sequences of every order"
    - "on the expected complexity of voronoi diagrams on terrains"
    - "on the expected complexity of random convex hulls"

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.CG(Computational Geometry)