human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "localization in internets of mobile agents a linear approach"):
  Abstract: Fifth generation~(5G) networks providing much higher bandwidth and faster data rates will allow connecting vast number of static and mobile devices, sensors, agents, users, machines, and vehicles, supporting Internet-of-Things (IoT), real-time dynamic networks of mobile things. Positioning and location awareness will become increasingly important, enabling deployment of new services and contributing to significantly improving the overall performance of the 5G~system. Many of the currently talked about solutions to positioning in~5G are centralized, mostly requiring direct line-of-sight (LoS) to deployed access nodes or anchors at the same time, which in turn requires high-density deployments of anchors. But these LoS and centralized positioning solutions may become unwieldy as the number of users and devices continues to grow without limit in sight. As an alternative to the centralized solutions, this paper discusses distributed localization in a 5G enabled IoT environment where many low power devices, users, or agents are to locate themselves without global or LoS access to anchors. Even though positioning is essentially a non-linear problem (solving circle equations by trilateration or triangulation), we discuss a cooperative \textit{linear} distributed iterative solution with only local measurements, communication and computation needed at each agent. Linearity is obtained by reparametrization of the agent location through barycentric coordinate representations based on local neighborhood geometry that may be computed in terms of certain Cayley-Menger determinants involving relative local inter-agent distance measurements.

Includes members:
  Member "an opportunistic linear convex algorithm for localization in mobile robot networks":
    also connected through other hyperedges:
      (none)
  Member "localization in mobile networks via virtual convex hulls":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a bayesian algorithm for distributed network localization using distance and direction data")
  Member "diland an algorithm for distributed sensor localization with noisy distance measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a bayesian algorithm for distributed network localization using distance and direction data")
  Member "higher dimensional consensus learning in large scale networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cooperative network synchronization asymptotic analysis")
  Member "fundamental limits of wideband localization part i a general framework":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hybrid lifi and wifi networks a survey")
  Member "distributed sensor localization in random environments using minimal number of anchor nodes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed joint sensor registration and multitarget tracking via sensor network")
  Member "distributed consensus algorithms in sensor networks quantized data and random link failures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully decentralized multi agent reinforcement learning with networked agents")

Question: Please tell me which class the source paper 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)