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 "a mountaintop view requires minimal sorting a faster contour tree algorithm"):
  Abstract: Consider a scalar field $f:\mathbb{M} \mapsto \mathbb{R}$, where $\mathbb{M}$ is a triangulated simplicial mesh in $\mathbb{R}^d$. A level set, or contour, at value $v$ is a connected component of $f^{-1}(v)$. As $v$ is changed, these contours change topology, merge into each other, or split. \emph{Contour trees} are concise representations of $f$ that track this contour behavior. The vertices of these trees are the critical points of $f$, where the gradient is zero. The edges represent changes in the topology of contours. It is a fundamental data structure in data analysis and visualization, and there is significant previous work (both theoretical and practical) on algorithms for constructing contour trees. #R##N#Suppose $\mathbb{M}$ has $n$ vertices, $N$ facets, and $t$ critical points. A classic result of Carr, Snoeyink, and Axen (2000) gives an algorithm that takes $O(n\log n + N\alpha(N))$ time (where $\alpha(\cdot)$ is the inverse Ackermann function). A further improvement to $O(t\log t + N)$ time was given by Chiang et al. All these algorithms involve a global sort of the critical points, a significant computational bottleneck. Unfortunately, lower bounds of $\Omega(t\log t)$ also exist. #R##N#We present the first algorithm that can avoid the global sort and has a refined time complexity that depends on the contour tree structure. Intuitively, if the tree is short and fat, we get significant improvements in running time. For a partition of the contour tree into a set of descending paths, $P$, our algorithm runs in $O(\sum_{p\in P} |p|\log|p| + t\alpha(t)+N)$ time. This is at most $O(t\log D + N)$, where $D$ is the diameter of the contour tree. Moreover, it is $O(t\alpha(t)+N)$ for balanced trees, a significant improvement over the previous complexity. We also prove lower bounds showing that the $\sum_{p\in P} |p|\log|p|$ complexity is inherent to computing contour trees.

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 "avoiding the global sort a faster contour tree algorithm":
    also connected through other hyperedges:
      (none)

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)