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 "in field peduncle detection of sweet peppers for robotic harvesting a comparative study"):
  Abstract: Robotic harvesting of crops has the potential to disrupt current agricultural practices. A key element to enabling robotic harvesting is to safely remove the crop from the plant which often involves locating and cutting the peduncle, the part of the crop that attaches it to the main stem of the plant. #R##N#In this paper we present a comparative study of two methods for performing peduncle detection. The first method is based on classic colour and geometric features obtained from the scene with a support vector machine classifier, referred to as PFH-SVM. The second method is an efficient deep neural network approach, MiniInception, that is able to be deployed on a robotic platform. In both cases we employ a secondary filtering process that enforces reasonable assumptions about the crop structure, such as the proximity of the peduncle to the crop. Our tests are conducted on Harvey, a sweet pepper harvesting robot, and is evaluated in a greenhouse using two varieties of sweet pepper, Ducati and Mercuno. We demonstrate that the MiniInception method achieves impressive accuracy and considerably outperforms the PFH-SVM approach achieving an F1 score of 0.564 and 0.302 respectively.

Includes members:
  Member "peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3 d information":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards active robotic vision in agriculture a deep learning approach to visual servoing in occluded and unstructured protected cropping environments")
  Member "autonomous sweet pepper harvesting for protected cropping systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flower interaction subsystem for a precision pollination robot")
  Member "rethinking the inception architecture for computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")

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)