human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "convex hull of imprecise points in o n log n time after preprocessing"):
  Abstract: Motivated by the desire to cope with \emph{data imprecision} \cite{Loeffler09}, we study methods for preprocessing a set of planar regions such that whenever we are given a set of points, each of which lies on a distinct region, we can compute a specified structure on these points more efficiently than in "standard settings" (that is, without preprocessing). In particular, we study two related problems. First, given a set $L$ of $n$ lines in the plane, we wish to preprocess $L$ such that later, upon receiving a set $P$ of $n$ points, each of which lies on a distinct line of $L$, we can construct the convex hull of $P$ efficiently. We show that in quadratic time and space it is possible to construct a data structure on $L$ that enables us to compute the convex hull of any such point set $P$ in $O(n \alpha(n) \log^* n)$ expected time. If we further assume that the points are "oblivious" with respect to the data structure, the running time improves to $O(n \alpha(n))$. The analysis applies almost verbatim when $L$ is a set of line-segments, and yields similar asymptotic bounds. We present several extensions, including a trade-off between space and query time and an output-sensitive algorithm. Second, we study the dual setting. Here, we are given a planar $n$-point set $P$, and we wish to preprocess $P$ such that given any set of lines $L$, each of which is incident to a distinct point of $P$, we can compute the $(\leq k)$-level of $L$ efficiently. Again, with quadratic preprocessing and storage, we obtain a data structure for $P$ such that for any such $L$, the $(\leq k)$-level can be constructed in expected time $O(n\alpha(n)(\log^*n - \log^* k) + nk)$. The bound improves to $O(n \alpha(n) + nk)$ if we assume obliviousness.

Includes members:
  Member "instance optimal geometric algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic distribution sensitive point location")
  Member "triangulating the square and squaring the triangle quadtrees and delaunay triangulations are equivalent":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spanning trees in multipartite geometric graphs")
  Member "shape fitting on point sets with probability distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "shortest path centrality and the all pairs shortest paths problem via vc dimension theory")
  Member "randomization yields simple o n log star n algorithms for difficult omega n problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "detecting weakly simple polygons")

Question: Please tell me which class the source paper 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)