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 "using neural networks to generate information maps for mobile sensors"):
  Abstract: Target localization is a critical task for mobile sensors and has many applications. However, generating informative trajectories for these sensors is a challenging research problem. A common method uses information maps that estimate the value of taking measurements from any point in the sensor state space. These information maps are used to generate trajectories; for example, a trajectory might be designed so its distribution of measurements matches the distribution of the information map. Regardless of the trajectory generation method, generating information maps as new observations are made is critical. However, it can be challenging to compute these maps in real-time. We propose using convolutional neural networks to generate information maps from a target estimate and sensor model in real-time. Simulations show that maps are accurately rendered while offering orders of magnitude reduction in computation time.

Includes members:
  Member "efficient and low cost localization of radio signals with a multirotor uav":
    also connected through other hyperedges:
      (none)
  Member "ergodic coverage in constrained environments using stochastic trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active area coverage from equilibrium")
  Member "real time area coverage and target localization using receding horizon ergodic exploration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized ergodic control distribution driven sensing and exploration for multiagent systems")
  Member "ergodic exploration of distributed information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "active area coverage from equilibrium")
  Member "convolutional neural networks at constrained time cost":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic image networks for human action recognition")

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)