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: "low cost and faster tracking systems using core sets for pose estimation"
  Abstract: How can a \$20 toy quadcopter hover using a weak "Internet of Things" mini-board and a web-cam? In the pose-estimation problem we need to rotate a set of $n$ marker (points) and choose one of their n! permutations, so that the sum of squared corresponding distances to another ordered set of $n$ markers is minimized. A popular heuristic for this problem is ICP. #R##N#We prove that \emph{every} set has a weighted subset (core-set) of constant size (independent of $n$), such that computing the optimal orientation of the small core-set would yield \emph{exactly} the same result as using the full set of $n$ markers. A deterministic algorithm for computing this core-set in $O(n)$ time is provided, using the Caratheodory Theorem from computational geometry. #R##N#We developed a system that enables low-cost and real-time tracking by computing this core-set on the cloud in one thread, and uses the last computed core-set locally in a parallel thread. Our experimental results show how these core-sets can boost the tracking time and quality for large and even small sets of both IR and RGB markers on a toy quadcopter. Open source code for the system and algorithm is provided

Connected through hyperedge HE-1 (induced by source paper "on geometric alignment in low doubling dimension"):
  with members:
    - "faster algorithms for the geometric transportation problem"
    - "node2vec scalable feature learning for networks"
    - "exploiting similarities among languages for machine translation"
    - "fast construction of nets in low dimensional metrics and their applications"

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.RO(Robotics)