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: "approximate range counting revisited"
  Abstract: This work presents new results on approximate range counting. If the actual count is $k$, then the data structures in this paper output a value, $\tau$, lying in the range $[(1-\varepsilon)k,(1+\varepsilon)k]$. The main results are the following: #R##N#(1) A new technique for efficiently solving any approximate range counting problem is presented. This technique can be viewed as an enhancement of Aronov and Har-Peled's technique [SIAM Journal of Computing, 2008]. The key reasons are the following: #R##N#(a) The new technique is sensitive to the value of $k$: As an application, this work presents a structure for approximate halfspace range counting in $R^d, d\geq 4$, which occupies $O(n)$ space and solves the query in roughly $O\left((n/k)^{1-1/\lfloor d/2\rfloor}\right)$ time. When $k=\Theta(n)$, then the query time is roughly $\tilde{O}(1)$. #R##N#(b) The new technique handles colored range searching problems: As an application, the orthogonal colored range counting problem is solved. Existing structures for exact counting use $O(n^d)$ space to answer the query in $O(\text{polylog } n)$ query time. Therefore, if one is willing for an approximation, an attractive result is obtained: an $O(n \text{ polylog } n)$ space data structure and an $O(\text{polylog } n)$ query time algorithm. #R##N#(2) An optimal solution for some approximate rectangle stabbing counting problems in $R^2$. This is achieved by a non-trivial reduction to planar point location. #R##N#(3) Finally, an efficient solution is obtained for $3$-sided orthogonal colored range counting. The result is obtained by a non-trivial combination of two different types of random sampling techniques and a reduction to non-colored range searching problem.

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)