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 in mobile networks via virtual convex hulls"
  Abstract: In this paper, we develop a \textit{distributed} algorithm to localize an arbitrary number of agents moving in a bounded region of interest. We assume that the network contains \textit{at least one} agent with known location (hereinafter referred to as an anchor), and each agent measures a noisy version of its motion and the distances to the nearby agents. We provide a~\emph{geometric approach}, which allows each agent to: (i) continually update the distances to the locations where it has exchanged information with the other nodes in the past; and (ii) measure the distance between a neighbor and any such locations. Based on this approach, we provide a \emph{linear update} to find the locations of an arbitrary number of mobile agents when they follow some convexity in their deployment and motion. #R##N#Since the agents are mobile, they may not be able to find nearby nodes (agents and/or anchors) to implement a distributed algorithm. To address this issue, we introduce the notion of a \emph{virtual convex hull} with the help of the aforementioned geometric approach. In particular, each agent keeps track of a virtual convex hull of other nodes, which may not physically exist, and updates its location with respect to its neighbors in the virtual hull. We show that the corresponding localization algorithm, in the absence of noise, can be abstracted as a Linear Time-Varying (LTV) system, with non-deterministic system matrices, which asymptotically tracks the true locations of the agents. We provide simulations to verify the analytical results and evaluate the performance of the algorithm in the presence of noise on the motion as well as on the distance measurements.

Connected through hyperedge HE-1 (induced by source paper "localization in internets of mobile agents a linear approach"):
  with members:
    - "an opportunistic linear convex algorithm for localization in mobile robot networks"
    - "diland an algorithm for distributed sensor localization with noisy distance measurements"
    - "higher dimensional consensus learning in large scale networks"
    - "fundamental limits of wideband localization part i a general framework"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-2 (induced by source paper "a bayesian algorithm for distributed network localization using distance and direction data"):
  with members:
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "joint device positioning and clock synchronization in 5g ultra dense networks"
    - "cooperative localization for mobile networks a distributed belief propagation mean field message passing algorithm"
    - "a tractable analysis of the improvement in unique localizability through collaboration"
    - "sub nanosecond time of flight on commercial wi fi cards"
    - "loopy belief propagation for approximate inference an empirical study"
    - "convergent message passing algorithms a unifying view"
    - "cooperative and distributed localization for wireless sensor networks in multipath environments"

Connected through hyperedge HE-3 (induced by source paper "an opportunistic linear convex algorithm for localization in mobile robot networks"):
  with members:
    - "asymptotic stability of stochastic ltv systems with applications to distributed dynamic fusion"
    - "markov localization for mobile robots in dynamic environments"

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)