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: "weednet dense semantic weed classification using multispectral images and mav for smart farming"
  Abstract: Selective weed treatment is a critical step in autonomous crop management as related to crop health and yield. However, a key challenge is reliable, and accurate weed detection to minimize damage to surrounding plants. In this paper, we present an approach for dense semantic weed classification with multispectral images collected by a micro aerial vehicle (MAV). We use the recently developed encoder-decoder cascaded Convolutional Neural Network (CNN), Segnet, that infers dense semantic classes while allowing any number of input image channels and class balancing with our sugar beet and weed datasets. To obtain training datasets, we established an experimental field with varying herbicide levels resulting in field plots containing only either crop or weed, enabling us to use the Normalized Difference Vegetation Index (NDVI) as a distinguishable feature for automatic ground truth generation. We train 6 models with different numbers of input channels and condition (fine-tune) it to achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested for MAV integration. Dataset used in this paper is released to support the community and future work.

Connected through hyperedge HE-1 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "realtime scheduling and power allocation using deep neural networks"
    - "over the air deep learning based radio signal classification"
    - "active learning for wireless iot intrusion detection"
    - "electrosense open and big spectrum data"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "adversarial attacks on deep learning based radio signal classification"

Connected through hyperedge HE-2 (induced by source paper "building an aerial ground robotics system for precision farming"):
  with members:
    - "semantic mapping for orchard environments by merging two sides reconstructions of tree rows"
    - "agricolmap aerial ground collaborative 3d mapping for precision farming"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "build your own visual inertial drone a cost effective and open source autonomous drone"
    - "an informative path planning framework for uav based terrain monitoring"
    - "an effective multi cue positioning system for agricultural robotics"
    - "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming"

Connected through 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"):
  with members:
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "deep learning in agriculture a survey"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "automatic model based dataset generation for fast and accurate crop and weeds detection"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "an informative path planning framework for uav based terrain monitoring"):
  with members:
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"
    - "a data driven approach for autonomous motion planning and control in off road driving scenarios"
    - "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "dynamic system identification and control for a cost effective open source vtol mav"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "pomdp lite for robust robot planning under uncertainty"

Connected through hyperedge HE-5 (induced by source paper "joint stem detection and crop weed classification for plant specific treatment in precision farming"):
  with members:
    - "from plants to landmarks time invariant plant localization that uses deep pose regression in agricultural fields"
    - "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-6 (induced by source paper "the rosario dataset multisensor data for localization and mapping in agricultural environments"):
  with members:
    - "3d soil compaction mapping through kriging based exploration with a mobile robot"
    - "multispecies fruit flower detection using a refined semantic segmentation network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "automatic model based dataset generation for fast and accurate crop and weeds detection"

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.CV(Computer Vision and Pattern Recognition)