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: "towards multi class object detection in unconstrained remote sensing imagery"
  Abstract: Automatic multi-class object detection in remote sensing images in unconstrained scenarios is of high interest for several applications including traffic monitoring and disaster management. The huge variation in object scale, orientation, category, and complex backgrounds, as well as the different camera sensors pose great challenges for current algorithms. In this work, we propose a new method consisting of a novel joint image cascade and feature pyramid network with multi-size convolution kernels to extract multi-scale strong and weak semantic features. These features are fed into rotation-based region proposal and region of interest networks to produce object detections. Finally, rotational non-maximum suppression is applied to remove redundant detections. During training, we minimize joint horizontal and oriented bounding box loss functions, as well as a novel loss that enforces oriented boxes to be rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on oriented bounding box detection tasks on the challenging DOTA dataset, outperforming all published methods by a large margin (+6% and +12% absolute improvement, respectively). Furthermore, it generalizes to two other datasets, NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines even when trained on DOTA. Our method can be deployed in multi-class object detection applications, regardless of the image and object scales and orientations, making it a great choice for unconstrained aerial and satellite imagery.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "a novel multi attention driven system for multi label remote sensing image classification"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "building instance classification using street view images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-4 (induced by source paper "bigearthnet deep learning models with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "building instance classification using street view images"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "learning modulated loss for rotated object detection"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "reppoints point set representation for object detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "foveabox beyond anchor based object detector"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-6 (induced by source paper "r3det refined single stage detector with feature refinement for rotating object"):
  with members:
    - "cascade retinanet maintaining consistency for single stage object detection"
    - "hybrid task cascade for instance segmentation"
    - "propose and attend single shot detector"
    - "rethinking classification and localization for cascade r cnn"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "scrdet towards more robust detection for small cluttered and rotated objects"
    - "fots fast oriented text spotting with a unified network"

Connected through hyperedge HE-7 (induced by source paper "gliding vertex on the horizontal bounding box for multi oriented object detection"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "centernet keypoint triplets for object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "rotation sensitive regression for oriented scene text detection"

Connected through hyperedge HE-8 (induced by source paper "oriented objects as pairs of middle lines"):
  with members:
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "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"
    - "fots fast oriented text spotting with a unified network"

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)