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 "collaborative high accuracy localization in mobile multipath environments"):
  Abstract: We study the problem of high accuracy localization of mobile nodes in a multipath-rich environment where sub-meter accuracies are required. We employ a peer-to-peer framework where the vehicles/nodes can get pairwise multipath-degraded ranging estimates in local neighborhoods together with a fixed number of anchor nodes. The challenge is to overcome the multipath-barrier with redundancy in order to provide the desired accuracies especially under severe multipath conditions when the fraction of received signals corrupted by multipath is dominating. We invoke a analytical graphical model framework based on particle filtering and reveal its high accuracy localization promise through simulations. We also address design questions such as "How many anchors and what fraction of line-of-sight (LOS) measurements are needed to achieve a specified target accuracy?", by analytically characterizing the performance improvement in localization accuracy as a function of the number of nodes in the network and the fraction of LOS measurements. In particular, for a static node placement, we show that the Cramer-Rao Lower Bound (CRLB), a fundamental lower bound on the localization accuracy, can be expressed as a product of two factors - a scalar function that depends only on the parameters of the noise distribution and a matrix that depends only on the geometry of node locations and the underlying connectivity graph. Further, a simplified expression is obtained for the CRLB that helps deduce the scaling behavior of the estimation error as a function of the number of agents and anchors in the network. The bound suggests that even a small fraction of LOS measurements can provide significant improvements. Conversely, a small fraction of NLOS measurements can significantly degrade the performance. The analysis is extended to the mobile setting and the performance is compared with the derived CRLB.

Includes members:
  Member "a measurement based shadow fading model for vehicle to vehicle network simulations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a measurement based multilink shadowing model for v2v network simulations of highway scenarios")
  Member "cooperative and distributed localization for wireless sensor networks in multipath environments":
    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 "distributed high accuracy peer to peer localization in mobile multipath environments":
    also connected through other hyperedges:
      (none)
  Member "fundamental limits of wideband localization part ii cooperative networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "peregrine network localization and navigation with scalable inference and efficient operation")

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.NI(Networking and Internet Architecture)