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 "building an aerial ground robotics system for precision farming"):
  Abstract: The application of autonomous robots in agriculture is gaining more and more popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, minimization of the human effort and maximization of yield. The Flourish research project faced this challenge by developing an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with flexible targeted intervention performed by multi-purpose agricultural unmanned ground vehicles (UGVs). This paper presents an exhaustive overview of the scientific and technological advances and outcomes obtained in the Flourish project. We introduce multi-spectral perception algorithms and aerial and ground based systems developed to monitor crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems to deal with the specificity of the employed robots and of the agricultural environment, highlighting the collaborative modules that enable the UAVs and UGVs to collect and share information in a unified environment model. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe here a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying treatments in a totally autonomous way.

Includes members:
  Member "semantic mapping for orchard environments by merging two sides reconstructions of tree rows":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion")
  Member "agricolmap aerial ground collaborative 3d mapping for precision farming":
    also connected through other hyperedges:
      (none)
  Member "maplab an open framework for research in visual inertial mapping and localization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "whole body control of a mobile manipulator using end to end reinforcement learning")
  Member "fully convolutional networks with sequential information for robust crop and weed detection in precision farming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming")
  Member "build your own visual inertial drone a cost effective and open source autonomous drone":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "duckiefloat a collision tolerant resource constrained blimp for long term autonomy in subterranean environments")
  Member "an informative path planning framework for uav based terrain monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "informative path planning and mapping for active sensing under localization uncertainty")
  Member "an effective multi cue positioning system for agricultural robotics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "agricolmap aerial ground collaborative 3d mapping for precision farming")
  Member "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scene and environment monitoring using aerial imagery and deep learning")

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)