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: "dota a large scale dataset for object detection in aerial images"
  Abstract: Object detection is an important and challenging problem in computer vision. Although the past decade has witnessed major advances in object detection in natural scenes, such successes have been slow to aerial imagery, not only because of the huge variation in the scale, orientation and shape of the object instances on the earth's surface, but also due to the scarcity of well-annotated datasets of objects in aerial scenes. To advance object detection research in Earth Vision, also known as Earth Observation and Remote Sensing, we introduce a large-scale Dataset for Object deTection in Aerial images (DOTA). To this end, we collect $2806$ aerial images from different sensors and platforms. Each image is of the size about 4000-by-4000 pixels and contains objects exhibiting a wide variety of scales, orientations, and shapes. These DOTA images are then annotated by experts in aerial image interpretation using $15$ common object categories. The fully annotated DOTA images contains $188,282$ instances, each of which is labeled by an arbitrary (8 d.o.f.) quadrilateral To build a baseline for object detection in Earth Vision, we evaluate state-of-the-art object detection algorithms on DOTA. Experiments demonstrate that DOTA well represents real Earth Vision applications and are quite challenging.

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 "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-3 (induced by source paper "arbitrary oriented object detection with circular smooth label"):
  with members:
    - "objects detection for remote sensing images based on polar coordinates"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"
    - "centernet keypoint triplets for object detection"
    - "adaptive period embedding for representing oriented objects in aerial images"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "gliding vertex on the horizontal bounding box for multi oriented object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"

Connected through hyperedge HE-4 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-5 (induced by source paper "objects detection for remote sensing images based on polar coordinates"):
  with members:
    - "x linenet detecting aircraft in remote sensing images by a pair of intersecting line segments"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "oriented objects as pairs of middle lines"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "scrdet towards more robust detection for small cluttered and rotated objects"

Connected through hyperedge HE-6 (induced by source paper "rsvqa visual question answering for remote sensing data"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "answer them all toward universal visual question answering models"
    - "learning to count objects in natural images for visual question answering"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-7 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-8 (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"

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)