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: "on the separability of stochastic geometric objects with applications"
  Abstract: In this paper, we study the linear separability problem for stochastic geometric objects under the well-known unipoint/multipoint uncertainty models. Let $S=S_R \cup S_B$ be a given set of stochastic bichromatic points, and define $n = \min\{|S_R|, |S_B|\}$ and $N = \max\{|S_R|, |S_B|\}$. We show that the separable-probability (SP) of $S$ can be computed in $O(nN^{d-1})$ time for $d \geq 3$ and $O(\min\{nN \log N, N^2\})$ time for $d=2$, while the expected separation-margin (ESM) of $S$ can be computed in $O(nN^{d})$ time for $d \geq 2$. In addition, we give an $\Omega(nN^{d-1})$ witness-based lower bound for computing SP, which implies the optimality of our algorithm among all those in this category. Also, a hardness result for computing ESM is given to show the difficulty of further improving our algorithm. As an extension, we generalize the same problems from points to general geometric objects, i.e., polytopes and/or balls, and extend our algorithms to solve the generalized SP and ESM problems in $O(nN^{d})$ and $O(nN^{d+1})$ time, respectively. Finally, we present some applications of our algorithms to stochastic convex-hull related problems.

Connected through hyperedge HE-1 (induced by source paper "computing shapley values in the plane"):
  with members:
    - "batched point location in sinr diagrams via algebraic tools"
    - "convex hulls under uncertainty"
    - "area and perimeter of the convex hull of stochastic points"
    - "computing the distance between piecewise linear bivariate functions"

Connected through hyperedge HE-2 (induced by source paper "on the expected diameter width and complexity of a stochastic convex hull"):
  with members:
    - "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"
    - "colored stochastic dominance problems"
    - "nearest neighbor searching under uncertainty ii"
    - "epsilon kernel coresets for stochastic points"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"

Connected through hyperedge HE-3 (induced by source paper "stochastic closest pair problem and most likely nearest neighbor search in tree spaces"):
  with members:
    - "nearest neighbor searching under uncertainty ii"
    - "convex hulls under uncertainty"
    - "approximating the expected values for combinatorial optimization problems over stochastic points"
    - "the geometric stability of voronoi diagrams with respect to small changes of the sites"

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)