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: "area and perimeter of the convex hull of stochastic points"
  Abstract: Given a set $P$ of $n$ points in the plane, we study the computation of the probability distribution function of both the area and perimeter of the convex hull of a random subset $S$ of $P$. The random subset $S$ is formed by drawing each point $p$ of $P$ independently with a given rational probability $\pi_p$. For both measures of the convex hull, we show that it is \#P-hard to compute the probability that the measure is at least a given bound $w$. For $\varepsilon\in(0,1)$, we provide an algorithm that runs in $O(n^{6}/\varepsilon)$ time and returns a value that is between the probability that the area is at least $w$, and the probability that the area is at least $(1-\varepsilon)w$. For the perimeter, we show a similar algorithm running in $O(n^{6}/\varepsilon)$ time. Finally, given $\varepsilon,\delta\in(0,1)$ and for any measure, we show an $O(n\log n+ (n/\varepsilon^2)\log(1/\delta))$-time Monte Carlo algorithm that returns a value that, with probability of success at least $1-\delta$, differs at most $\varepsilon$ from the probability that the measure is at least $w$.

Connected through hyperedge HE-1 (induced by source paper "solving linear program with chubanov queries and bisection moves"):
  with members:
    - "exact algorithms for semidefinite programs with degenerate feasible set"

Connected through hyperedge HE-2 (induced by source paper "computing shapley values in the plane"):
  with members:
    - "on the separability of stochastic geometric objects with applications"
    - "batched point location in sinr diagrams via algebraic tools"
    - "convex hulls under uncertainty"
    - "computing the distance between piecewise linear bivariate functions"

Connected through hyperedge HE-3 (induced by source paper "convex hulls under uncertainty"):
  with members:
    - "nearest neighbor searching under uncertainty ii"

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)