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: "a large contextual dataset for classification detection and counting of cars with deep learning"
  Abstract: We have created a large diverse set of cars from overhead images, which are useful for training a deep learner to binary classify, detect and count them. The dataset and all related material will be made publically available. The set contains contextual matter to aid in identification of difficult targets. We demonstrate classification and detection on this dataset using a neural network we call ResCeption. This network combines residual learning with Inception-style layers and is used to count cars in one look. This is a new way to count objects rather than by localization or density estimation. It is fairly accurate, fast and easy to implement. Additionally, the counting method is not car or scene specific. It would be easy to train this method to count other kinds of objects and counting over new scenes requires no extra set up or assumptions about object locations.

Connected through hyperedge HE-1 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "gabor convolutional networks"
    - "recent advances in object detection in the age of deep convolutional neural networks"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "deep adaptive proposal network for object detection in optical remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "drone based rgbt vehicle detection and counting a challenge"):
  with members:
    - "nwpu crowd a large scale benchmark for crowd counting"
    - "locality constrained spatial transformer network for video crowd counting"
    - "perspective guided convolution networks for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "mrcnet crowd counting and density map estimation in aerial and ground imagery"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-3 (induced by source paper "counting dense objects in remote sensing images"):
  with members:
    - "guided attention network for object detection and counting on drones"
    - "learning from synthetic data for crowd counting in the wild"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "cbam convolutional block attention module"
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "dota a large scale dataset for object detection in aerial images"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "on learning vehicle detection in satellite video"):
  with members:
    - "clustered object detection in aerial images"
    - "error bounded foreground and background modeling for moving object detection in satellite videos"
    - "multi cue vehicle detection for semantic video compression in georegistered aerial videos"
    - "yolov3 an incremental improvement"
    - "detecting tiny moving vehicles in satellite videos"
    - "clusternet detecting small objects in large scenes by exploiting spatio temporal information"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "moving object detection by detecting contiguous outliers in the low rank representation"

Connected through hyperedge HE-5 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-6 (induced by source paper "ai enabling technologies a survey"):
  with members:
    - "the lustre storage architecture"
    - "qiniu submission to activitynet challenge 2018"
    - "moments in time dataset one million videos for event understanding"
    - "interactive supercomputing on 40 000 cores for machine learning and data analysis"
    - "this looks like that deep learning for interpretable image recognition"
    - "gazelle a low latency framework for secure neural network inference"
    - "tracking emerges by colorizing videos"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-7 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-8 (induced by source paper "precise detection in densely packed scenes"):
  with members:
    - "deep learning for generic object detection a survey"
    - "acquisition of localization confidence for accurate object detection"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft nms improving object detection with one line of code"
    - "improving object localization with fitness nms and bounded iou loss"
    - "focal loss for dense object detection"
    - "citypersons a diverse dataset for pedestrian detection"
    - "learning non maximum suppression"

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)