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: "an optimal algorithm for computing the spherical depth of points in the plane"
  Abstract: For a distribution function $F$ on $\mathbb{R}^d$ and a point $q\in \mathbb{R}^d$, the \emph{spherical depth} $\SphD(q;F)$ is defined to be the probability that a point $q$ is contained inside a random closed hyper-ball obtained from a pair of points from $F$. The spherical depth $\SphD(q;S)$ is also defined for an arbitrary data set $S\subseteq \mathbb{R}^d$ and $q\in \mathbb{R}^d$. This definition is based on counting all of the closed hyper-balls, obtained from pairs of points in $S$, that contain $q$. The significant advantage of using the spherical depth in multivariate data analysis is related to its complexity of computation. Unlike most other data depths, the time complexity of the spherical depth grows linearly rather than exponentially in the dimension $d$. The straightforward algorithm for computing the spherical depth in dimension $d$ takes $O(dn^2)$. The main result of this paper is an optimal algorithm that we present for computing the bivariate spherical depth. The algorithm takes $O(n \log n)$ time. By reducing the problem of \textit{Element Uniqueness}, we prove that computing the spherical depth requires $\Omega(n \log n)$ time. Some geometric properties of spherical depth are also investigated in this paper. These properties indicate that \emph{simplicial depth} ($\SD$) (Liu, 1990) is linearly bounded by spherical depth (in particular, $\SphD\geq \frac{2}{3}SD$). To illustrate this relationship between the spherical depth and the simplicial depth, some experimental results are provided. The obtained experimental bound ($\SphD\geq 2\SD$) indicates that, perhaps, a stronger theoretical bound can be achieved.

Connected through hyperedge HE-1 (induced by source paper "algorithmic and geometric aspects of data depth with focus on β skeleton depth"):
  with members:
    - "computing the planar β skeleton depth"
    - "approximate data depth revisited"
    - "planar β skeletons via point location in monotone subdivisions of subset of lunes"
    - "on range searching with semialgebraic sets ii"
    - "dynamic maintenance of half space depth for points and contours"

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)