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: "drone based object counting by spatially regularized regional proposal network"
  Abstract: Existing counting methods often adopt regression-based approaches and cannot precisely localize the target objects, which hinders the further analysis (e.g., high-level understanding and fine-grained classification). In addition, most of prior work mainly focus on counting objects in static environments with fixed cameras. Motivated by the advent of unmanned flying vehicles (i.e., drones), we are interested in detecting and counting objects in such dynamic environments. We propose Layout Proposal Networks (LPNs) and spatial kernels to simultaneously count and localize target objects (e.g., cars) in videos recorded by the drone. Different from the conventional region proposal methods, we leverage the spatial layout information (e.g., cars often park regularly) and introduce these spatially regularized constraints into our network to improve the localization accuracy. To evaluate our counting method, we present a new large-scale car parking lot dataset (CARPK) that contains nearly 90,000 cars captured from different parking lots. To the best of our knowledge, it is the first and the largest drone view dataset that supports object counting, and provides the bounding box annotations.

Connected through hyperedge HE-1 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects"):
  with members:
    - "learning to calibrate straight lines for fisheye image rectification"
    - "unmanned aerial vehicles uavs a survey on civil applications and key research challenges"
    - "resource allocation for solar powered uav communication systems"
    - "throughput maximization for laser powered uav wireless communication systems"
    - "learning attraction field representation for robust line segment detection"
    - "anisotropic scale junction detection and matching for indoor images"
    - "deepmvs learning multi view stereopsis"
    - "the uavid dataset for video semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "university 1652 a multi view multi source benchmark for drone based geo localization"):
  with members:
    - "building information modeling and classification by visual learning at a city scale"
    - "triplet based deep hashing network for cross modal retrieval"
    - "lending orientation to neural networks for cross view geo localization"
    - "optimal feature transport for cross view image geo localization"
    - "vision meets drones a challenge"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "revisiting oxford and paris large scale image retrieval benchmarking"

Connected through hyperedge HE-3 (induced by source paper "approximate query service on autonomous iot cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "scaling video analytics on constrained edge nodes"
    - "vstore a data store for analytics on large videos"
    - "supporting video queries on zero streaming cameras"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "scanner efficient video analysis at scale"

Connected through hyperedge HE-4 (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-5 (induced by source paper "au air a multi modal unmanned aerial vehicle dataset for low altitude traffic surveillance"):
  with members:
    - "autonomous aerial cinematography in unstructured environments with learned artistic decision making"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "vision meets drones a challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the blackbird dataset a large scale dataset for uav perception in aggressive flight"
    - "yolov3 an incremental improvement"
    - "the unmanned aerial vehicle benchmark object detection and tracking"

Connected through hyperedge HE-6 (induced by source paper "multi level bottom top and top bottom feature fusion for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "inverse attention guided deep crowd counting network"
    - "crowd counting and density estimation by trellis encoder decoder network"
    - "learning from synthetic data for crowd counting in the wild"
    - "uncertainty guided multi scale residual learning using a cycle spinning cnn for single image de raining"
    - "deeply supervised salient object detection with short connections"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"

Connected through hyperedge HE-7 (induced by source paper "aerial multi object tracking by detection using deep association networks"):
  with members:
    - "ev imo motion segmentation dataset and learning pipeline for event cameras"
    - "online multi object tracking with dual matching attention networks"
    - "cascade r cnn high quality object detection and instance segmentation"
    - "deep continuous conditional random fields with asymmetric inter object constraints for online multi object tracking"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "cornernet detecting objects as paired keypoints"
    - "deep cosine metric learning for person re identification"
    - "event based moving object detection and tracking"

Connected through hyperedge HE-8 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

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)