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 "sensor assignment algorithms to improve observability while tracking targets"):
  Abstract: We study two sensor assignment problems for multi-target tracking with the goal of improving the observability of the underlying estimator. In the restricted version of the problem, we focus on assigning unique pairs of sensors to each target. We present a 1/3-approximation algorithm for this problem. We use the inverse of the condition number as the value function. If the target's motion model is not known, the inverse cannot be computed exactly. Instead, we present a lower bound for range-only sensing. #R##N#In the general version, the sensors must form teams to track individual targets. We do not force any specific constraints on the size of each team, instead assume that the value function is monotonically increasing and is submodular. A greedy algorithm that yields a 1/2-approximation. However, we show that the inverse of the condition number is neither monotone nor submodular. Instead, we present other measures that are monotone and submodular. In addition to theoretical results, we evaluate our results empirically through simulations.

Includes members:
  Member "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi objective multi agent planning for jointly discovering and tracking mobile object")
  Member "trackerbots autonomous unmanned aerial vehicle for real time localization and tracking of multiple radio tagged animals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi objective multi agent planning for jointly discovering and tracking mobile object")
  Member "distributed state estimation using intermittently connected robot networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed attack robust submodular maximization for multi robot planning")
  Member "active target tracking with self triggered communications in multi robot teams":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed assignment with limited communication for multi robot multi target tracking")
  Member "distributed algorithms for feature extraction off loading in multi camera visual sensor networks":
    also connected through other hyperedges:
      (none)
  Member "observability aware trajectory optimization for self calibration with application to uavs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "observability aware self calibration of visual and inertial sensors for ego motion estimation")
  Member "scheduling nonlinear sensors for stochastic process estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measurement scheduling for cooperative localization in resource constrained conditions")

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.RO(Robotics)