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: "from plants to landmarks time invariant plant localization that uses deep pose regression in agricultural fields"
  Abstract: Agricultural robots are expected to increase yields in a sustainable way and automate precision tasks, such as weeding and plant monitoring. At the same time, they move in a continuously changing, semi-structured field environment, in which features can hardly be found and reproduced at a later time. Challenges for Lidar and visual detection systems stem from the fact that plants can be very small, overlapping and have a steadily changing appearance. Therefore, a popular way to localize vehicles with high accuracy is based on ex- pensive global navigation satellite systems and not on natural landmarks. The contribution of this work is a novel image- based plant localization technique that uses the time-invariant stem emerging point as a reference. Our approach is based on a fully convolutional neural network that learns landmark localization from RGB and NIR image input in an end-to-end manner. The network performs pose regression to generate a plant location likelihood map. Our approach allows us to cope with visual variances of plants both for different species and different growth stages. We achieve high localization accuracies as shown in detailed evaluations of a sugar beet cultivation phase. In experiments with our BoniRob we demonstrate that detections can be robustly reproduced with centimeter accuracy.

Connected through hyperedge HE-1 (induced by source paper "visual servoing based navigation for monitoring row crop fields"):
  with members:
    - "an effective multi cue positioning system for agricultural robotics"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"

Connected through hyperedge HE-2 (induced by source paper "joint stem detection and crop weed classification for plant specific treatment in precision farming"):
  with members:
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "real time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in cnns"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fully convolutional networks for semantic segmentation"

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"
    - "autonomous sweet pepper harvesting for protected cropping systems"
    - "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 "an effective multi cue positioning system for agricultural robotics"):
  with members:
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"

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)