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: "localization and tracking in mobile networks virtual convex hulls and beyond"
  Abstract: In this paper, we discuss the problem of tracking the locations of an arbitrary number of agents moving in a bounded region. Assuming that each agent knows its motion precisely, and also its distances and angles to the nodes in its communication radius, we provide a \emph{geometric approach} to continually update the distances and angles even when the agents move out of range. Based on this approach, we provide a simple \emph{linear} update to track the locations of an arbitrary number of mobile agents when they follow some convexity in their deployment and motion, given at least $m+1$ agents whose locations are always known (hereinafter referred to as anchors) in ${\mathbb{R}}^{m}$. More precisely, this linear update requires the agents to intermittently move inside the convex hull of the anchors. #R##N#Since the agents are mobile, they may move inside and outside of the convex hull formed by the anchors and the above convexity in the deployment is not necessarily satisfied. To deal with such issues, we introduce the notion of a \emph{virtual convex hull} with the help of the aforementioned geometric approach. Based on the virtual hulls, we provide another algorithm where no agent is required to lie in any convex hull at any given time. Each agent only keeps track of a virtual convex hull, which may not physically exist, of other nodes (agents and/or anchors), and updates its location with respect to its neighbors in the virtual hull. We show that the corresponding localization algorithm can be abstracted as a Linear Time-Varying (LTV) system that asymptotically tracks the true locations of the agents. Finally, we show that exactly one anchor suffices to track the locations of an arbitrary number of mobile agents in $\mathbb{R}^m,m\geq 1$, using the approach described in this paper.

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.SY(Systems and Control)