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: "no dimensional tverberg theorems and algorithms"
  Abstract: Tverberg's theorem is a classic result in discrete geometry. It states that for any integer $k \ge 2$ and any finite $d$-dimensional point set $P \subseteq \mathbb{R}^d$ of at least $(d + 1)(k - 1) + 1$ points, we can partition $P$ into $k$ subsets whose convex hulls have a non-empty intersection. The computational problem of finding such a partition lies in the complexity class $\mathrm{PPAD} \cap \mathrm{PLS}$, but no hardness results are known. Tverberg's theorem also has a colorful variant: the points in $P$ have colors, and under certain conditions, $P$ can be partitioned into colorful sets, i.e., sets in which each color appears exactly once such that the convex hulls of the sets intersect. #R##N#Recently, Adiprasito, Barany, and Mustafa [SODA 2019] proved a no-dimensional version of Tverberg's theorem, in which the convex hulls of the sets in the partition may intersect in an approximate fashion, relaxing the requirement on the cardinality of $P$. The argument is constructive, but it does not result in a polynomial-time algorithm. #R##N#We present an alternative proof for a no-dimensional Tverberg theorem that leads to an efficient algorithm to find the partition. More specifically, we show an deterministic algorithm that finds for any set $P \subseteq \mathbb{R}^d$ of $n$ points and any $k \in \{2, \dots, n\}$ in $O(nd \log k )$ time a partition of $P$ into $k$ subsets such that there is a ball of radius $O(\frac{k}{\sqrt{n}}\textrm{diam}(P))$ intersecting the convex hull of each subset. A similar result holds also for the colorful version. #R##N#To obtain our result, we generalize Sarkaria's tensor product constructions [Israel Journal Math., 1992] that reduces the Tverberg problem to the Colorful Caratheodory problem. By carefully choosing the vectors used in the tensor products, we implement the reduction in an efficient manner.

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)