human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "autonomous sweet pepper harvesting for protected cropping systems"
  Abstract: In this letter, we present a new robotic harvester (Harvey) that can autonomously harvest sweet pepper in protected cropping environments. Our approach combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers. Initial field trials in protected cropping environments, with two cultivar, demonstrate the efficacy of this approach achieving a 46% success rate for unmodified crop, and 58% for modified crop. Furthermore, for the more favourable cultivar we were also able to detach 90% of sweet peppers, indicating that improvements in the grasping success rate would result in greatly improved harvesting performance.

Connected through hyperedge HE-1 (induced by source paper "flower interaction subsystem for a precision pollination robot"):
  with members:
    - "design of an autonomous precision pollination robot"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-2 (induced by source paper "towards active robotic vision in agriculture a deep learning approach to visual servoing in occluded and unstructured protected cropping environments"):
  with members:
    - "deep learning in agriculture a survey"
    - "peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3 d information"

Connected through hyperedge HE-3 (induced by source paper "an overview of perception methods for horticultural robots from pollination to harvest"):
  with members:
    - "fruit quantity and quality estimation using a robotic vision system"
    - "from plants to landmarks time invariant plant localization that uses deep pose regression in agricultural fields"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "design of an autonomous precision pollination robot"):
  with members:
    - "rethinking the inception architecture for computer vision"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "design and evaluation of a novel cable driven gripper with perception capabilities for strawberry picking robots"):
  with members:
    - "peduncle detection of sweet pepper for autonomous crop harvesting combined color and 3 d information"

Connected through hyperedge HE-6 (induced by source paper "fruit quantity and quality estimation using a robotic vision system"):
  with members:
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "enabling a pepper robot to provide automated and interactive tours of a robotics laboratory"):
  with members:
    - "setting up pepper for autonomous navigation and personalized interaction with users"

Connected through hyperedge HE-8 (induced by source paper "cartman the low cost cartesian manipulator that won the amazon robotics challenge"):
  with members:
    - "mechanical design of a cartesian manipulator for warehouse pick and place"
    - "design of a multi modal end effector and grasping system how integrated design helped win the amazon robotics challenge"
    - "semantic segmentation from limited training data"
    - "analysis and observations from the first amazon picking challenge"
    - "lessons from the amazon picking challenge"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"

Question: Please tell me which class the center node 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)