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: "learning roi transformer for detecting oriented objects in aerial images"
  Abstract: Object detection in aerial images is an active yet challenging task in computer vision because of the birdview perspective, the highly complex backgrounds, and the variant appearances of objects. Especially when detecting densely packed objects in aerial images, methods relying on horizontal proposals for common object detection often introduce mismatches between the Region of Interests (RoIs) and objects. This leads to the common misalignment between the final object classification confidence and localization accuracy. Although rotated anchors have been used to tackle this problem, the design of them always multiplies the number of anchors and dramatically increases the computational complexity. In this paper, we propose a RoI Transformer to address these problems. More precisely, to improve the quality of region proposals, we first designed a Rotated RoI (RRoI) learner to transform a Horizontal Region of Interest (HRoI) into a Rotated Region of Interest (RRoI). Based on the RRoIs, we then proposed a Rotated Position Sensitive RoI Align (RPS-RoI-Align) module to extract rotation-invariant features from them for boosting subsequent classification and regression. Our RoI Transformer is with light weight and can be easily embedded into detectors for oriented object detection. A simple implementation of the RoI Transformer has achieved state-of-the-art performances on two common and challenging aerial datasets, i.e., DOTA and HRSC2016, with a neglectable reduction to detection speed. Our RoI Transformer exceeds the deformable Position Sensitive RoI pooling when oriented bounding-box annotations are available. Extensive experiments have also validated the flexibility and effectiveness of our RoI Transformer. The results demonstrate that it can be easily integrated with other detector architectures and significantly improve the performances.

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 "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 "deep active learning for remote sensing object detection"):
  with members:
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "yolov3 an incremental improvement"
    - "cost effective object detection active sample mining with switchable selection criteria"
    - "towards human machine cooperation self supervised sample mining for object detection"
    - "robust face detection via learning small faces on hard images"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "active learning for cost sensitive classification"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "adaptive period embedding for representing oriented objects in aerial images"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "region proposal by guided anchoring"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "sliding line point regression for shape robust scene text detection"
    - "textboxes a single shot oriented scene text detector"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-7 (induced by source paper "clustered object detection in aerial images"):
  with members:
    - "high resolution representations for labeling pixels and regions"
    - "vision meets drones a challenge"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "focal loss for dense object detection"
    - "dota a large scale dataset for object detection in aerial images"
    - "cascade r cnn delving into high quality object detection"
    - "single shot refinement neural network for object detection"
    - "an analysis of scale invariance in object detection snip"

Connected through hyperedge HE-8 (induced by source paper "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "scene text detection with supervised pyramid context 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)