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 difference between closest furthest and orthogonal pairs nearly linear vs barely subquadratic complexity in computational geometry"
  Abstract: Point location problems for $n$ points in $d$-dimensional Euclidean space (and $\ell_p$ spaces more generally) have typically had two kinds of running-time solutions: #R##N#* (Nearly-Linear) less than $d^{poly(d)} \cdot n \log^{O(d)} n$ time, or #R##N#* (Barely-Subquadratic) $f(d) \cdot n^{2-1/\Theta(d)}$ time, for various $f$. #R##N#For small $d$ and large $n$, "nearly-linear" running times are generally feasible, while "barely-subquadratic" times are generally infeasible. For example, in the Euclidean metric, finding a Closest Pair among $n$ points in ${\mathbb R}^d$ is nearly-linear, solvable in $2^{O(d)} \cdot n \log^{O(1)} n$ time, while known algorithms for Furthest Pair (the diameter of the point set) are only barely-subquadratic, requiring $\Omega(n^{2-1/\Theta(d)})$ time. Why do these proximity problems have such different time complexities? Is there a barrier to obtaining nearly-linear algorithms for problems which are currently only barely-subquadratic? #R##N#We give a novel exact and deterministic self-reduction for the Orthogonal Vectors problem on $n$ vectors in $\{0,1\}^d$ to $n$ vectors in ${\mathbb Z}^{\omega(\log d)}$ that runs in $2^{o(d)}$ time. As a consequence, barely-subquadratic problems such as Euclidean diameter, Euclidean bichromatic closest pair, ray shooting, and incidence detection do not have $O(n^{2-\epsilon})$ time algorithms (in Turing models of computation) for dimensionality $d = \omega(\log \log n)^2$, unless the popular Orthogonal Vectors Conjecture and the Strong Exponential Time Hypothesis are false. That is, while poly-log-log-dimensional Closest Pair is in $n^{1+o(1)}$ time, the analogous case of Furthest Pair can encode larger-dimensional problems conjectured to require $n^{2-o(1)}$ time. We also show that the All-Nearest Neighbors problem in $\omega(\log n)$ dimensions requires $n^{2-o(1)}$ time to solve, assuming either of the above conjectures.

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)