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: "improved time space trade offs for computing voronoi diagrams"
  Abstract: Let $P$ be a planar set of $n$ sites in general position. For $k \in \{1, \dots, n-1\}$, the Voronoi diagram of order $k$ for $P$ is obtained by subdividing the plane into cells such that points in the same cell have the same set of nearest $k$ neighbors in $P$. The (nearest site) Voronoi diagram (NVD) and the farthest site Voronoi diagram (FVD) are the particular cases of $k=1$ and $k=n-1$, respectively. For any given $K \in \{1, \dots, n-1\}$, the family of all higher-order Voronoi diagrams of order $k = 1, \dots, K$ for $P$ can be computed in total time $O(nK^2+ n \log n)$ using $O(K^2(n-K))$ space [Aggarwal et al., DCG'89; Lee, TC'82]. Moreover, NVD and FVD for $P$ can be computed in $O(n\log n)$ time using $O(n)$ space [Preparata, Shamos, Springer'85].  For $s \in \{1, \dots, n\}$, an $s$-workspace algorithm has random access to a read-only array with the sites of $P$ in arbitrary order. Additionally, the algorithm may use $O(s)$ words, of $\Theta(\log n)$ bits each, for reading and writing intermediate data. The output can be written only once and cannot be accessed or modified afterwards.  We describe a deterministic $s$-workspace algorithm for computing NVD and FVD for $P$ that runs in $O((n^2/s)\log s)$ time. Moreover, we generalize our $s$-workspace algorithm so that for any given $K \in O(\sqrt{s})$, we compute the family of all higher-order Voronoi diagrams of order $k = 1, \dots, K$ for $P$ in total expected time $O\bigl(\frac{n^2 K^5}{s}(\log s + K \, 2^{O(\log^* K)}) \bigr)$ or in total deterministic time $O\bigl(\frac{n^2 K^5}{s}(\log s + K \log K) \bigr)$. Previously, for Voronoi diagrams, the only known $s$-workspace algorithm runs in expected time $O\bigl((n^2/s) \log s + n \log s \log^*s\bigr)$ [Korman et al., WADS'15] and only works for NVD (i.e., $k=1$). Unlike the previous algorithm, our new method is very simple and does not rely on advanced data structures or random sampling techniques.

Connected through hyperedge HE-1 (induced by source paper "space efficient algorithms for breadth depth search"):
  with members:
    - "space efficient algorithms for longest increasing subsequence"
    - "improved space efficient linear time algorithms for some classical graph problems"
    - "biconnectivity st numbering and other applications of dfs using o n bits"

Connected through hyperedge HE-2 (induced by source paper "computational geometry column 67"):
  with members:
    - "common tangents of two disjoint polygons in linear time and constant workspace"
    - "time space trade off for finding the k visibility region of a point in a polygon"
    - "an experimental study of algorithms for geodesic shortest paths in the constant workspace model"
    - "ime space trade offs for computing euclidean minimum spanning trees"
    - "time space trade offs for triangulations and voronoi diagrams"
    - "experimental study of compressed stack algorithms in limited memory environments"
    - "outer common tangents and nesting of convex hulls in linear time and constant workspace"
    - "an optimal algorithm for the separating common tangents of two polygons"

Connected through hyperedge HE-3 (induced by source paper "ime space trade offs for computing euclidean minimum spanning trees"):
  with members:
    - "shortest path in a polygon using sublinear space"
    - "memory constrained algorithms for simple polygons"
    - "computing a visibility polygon using few variables"

Connected through hyperedge HE-4 (induced by source paper "space efficient algorithms for longest increasing subsequence"):
  with members:
    - "on space efficiency of algorithms working on structural decompositions of graphs"
    - "estimating the longest increasing sequence in polylogarithmic time"
    - "space efficient streaming algorithms for the distance to monotonicity and asymmetric edit distance"

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)