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: "distribution sensitive bounds on relative approximations of geometric ranges"
  Abstract: A family $\mathcal{R}$ of ranges and a set $X$ of points together define a range space $(X, \mathcal{R}|_X)$, where $\mathcal{R}|_X = \{X \cap h \mid h \in \mathcal{R}\}$. We want to find a structure to estimate the quantity $|X \cap h|/|X|$ for any range $h \in \mathcal{R}$ with the $(\rho, \epsilon)$-guarantee: (i) if $|X \cap h|/|X| > \rho$, the estimate must have a relative error $\epsilon$; (ii) otherwise, the estimate must have an absolute error $\rho \epsilon$. The objective is to minimize the size of the structure. Currently, the dominant solution is to compute a relative $(\rho, \epsilon)$-approximation, which is a subset of $X$ with $\tilde{O}(\lambda/(\rho \epsilon^2))$ points, where $\lambda$ is the VC-dimension of $(X, \mathcal{R}|_X)$, and $\tilde{O}$ hides polylog factors. #R##N#This paper shows a more general bound sensitive to the content of $X$. We give a structure that stores $O(\log (1/\rho))$ integers plus $\tilde{O}(\theta \cdot (\lambda/\epsilon^2))$ points of $X$, where $\theta$ - called the disagreement coefficient - measures how much the ranges differ from each other in their intersections with $X$. The value of $\theta$ is between 1 and $1/\rho$, such that our space bound is never worse than that of relative $(\rho, \epsilon)$-approximations, but we improve the latter's $1/\rho$ term whenever $\theta = o(\frac{1}{\rho \log (1/\rho)})$. We also prove that, in the worst case, summaries with the $(\rho, 1/2)$-guarantee must consume $\Omega(\theta)$ words even for $d = 2$ and $\lambda \le 3$. #R##N#We then constrain $\mathcal{R}$ to be the set of halfspaces in $\mathbb{R}^d$ for a constant $d$, and prove the existence of structures with $o(1/(\rho \epsilon^2))$ size offering $(\rho,\epsilon)$-guarantees, when $X$ is generated from various stochastic distributions. This is the first formal justification on why the term $1/\rho$ is not compulsory for "realistic" inputs.

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)