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: "detecting mammals in uav images best practices to address a substantially imbalanced dataset with deep learning"
  Abstract: Abstract   Knowledge over the number of animals in large wildlife reserves is a vital necessity for park rangers in their efforts to protect endangered species. Manual animal censuses are dangerous and expensive, hence Unmanned Aerial Vehicles (UAVs) with consumer level digital cameras are becoming a popular alternative tool to estimate livestock. Several works have been proposed that semi-automatically process UAV images to detect animals, of which some employ Convolutional Neural Networks (CNNs), a recent family of deep learning algorithms that proved very effective in object detection in large datasets from computer vision. However, the majority of works related to wildlife focuses only on small datasets (typically subsets of UAV campaigns), which might be detrimental when presented with the sheer scale of real study areas for large mammal census. Methods may yield thousands of false alarms in such cases. In this paper, we study how to scale CNNs to large wildlife census tasks and present a number of recommendations to train a CNN on a large UAV dataset. We further introduce novel evaluation protocols that are tailored to censuses and model suitability for subsequent human verification of detections. Using our recommendations, we are able to train a CNN reducing the number of false positives by an order of magnitude compared to previous state-of-the-art. Setting the requirements at 90% recall, our CNN allows to reduce the amount of data required for manual verification by three times, thus making it possible for rangers to screen all the data acquired efficiently and to detect almost all animals in the reserve automatically.

Connected through hyperedge HE-1 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "drone based joint density map estimation localization and tracking with space time multi scale attention network"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "deep learning based aerial image classification for emergency response applications using unmanned aerial vehicles"
    - "a survey of deep learning based object detection"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "vision meets drones a challenge"

Connected through hyperedge HE-2 (induced by source paper "animal detection in man made environments"):
  with members:
    - "looking fast and slow memory guided mobile video object detection"
    - "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"
    - "the iwildcam 2019 challenge dataset"
    - "searching for mobilenetv3"
    - "the via annotation software for images audio and video"
    - "large scale interactive object segmentation with human annotators"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep learning for generic object detection a survey"

Connected through hyperedge HE-3 (induced by source paper "half a percent of labels is enough efficient animal detection in uav imagery using deep cnns and active learning"):
  with members:
    - "detecting animals in african savanna with uavs and the crowds"
    - "deep bayesian active learning with image data"
    - "instance normalization the missing ingredient for fast stylization"
    - "one shot learning with memory augmented neural networks"
    - "yolo9000 better faster stronger"
    - "you only look once unified real time object detection"
    - "optimal transport for domain adaptation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "workflow design analysis for high resolution satellite image analysis"):
  with members:
    - "automatically identifying counting and describing wild animals in camera trap images with deep learning"
    - "right whale recognition using convolutional neural networks"
    - "using pilot systems to execute many task workloads on supercomputers"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "an entropic optimal transport loss for learning deep neural networks under label noise in remote sensing images"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "a semi supervised two stage approach to learning from noisy labels"
    - "high resolution multispectral dataset for semantic segmentation"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "learning aerial image segmentation from online maps"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"

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)