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: "recurrent scale approximation for object detection in cnn"
  Abstract: Since convolutional neural network (CNN) lacks an inherent mechanism to handle large scale variations, we always need to compute feature maps multiple times for multi-scale object detection, which has the bottleneck of computational cost in practice. To address this, we devise a recurrent scale approximation (RSA) to compute feature map once only, and only through this map can we approximate the rest maps on other levels. At the core of RSA is the recursive rolling out mechanism: given an initial map at a particular scale, it generates the prediction at a smaller scale that is half the size of input. To further increase efficiency and accuracy, we (a): design a scale-forecast network to globally predict potential scales in the image since there is no need to compute maps on all levels of the pyramid. (b): propose a landmark retracing network (LRN) to trace back locations of the regressed landmarks and generate a confidence score for each landmark; LRN can effectively alleviate false positives caused by the accumulated error in RSA. The whole system can be trained end-to-end in a unified CNN framework. Experiments demonstrate that our proposed algorithm is superior against state-of-the-art methods on face detection benchmarks and achieves comparable results for generic proposal generation. The source code of RSA is available at github.com/sciencefans/RSA-for-object-detection.

Connected through hyperedge HE-1 (induced by source paper "revisiting the sibling head in object detector"):
  with members:
    - "reppoints point set representation for object detection"
    - "double head rcnn rethinking classification and localization for object detection"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "centernet keypoint triplets for object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"

Connected through hyperedge HE-2 (induced by source paper "kpnet towards minimal face detector"):
  with members:
    - "bottom up object detection by grouping extreme and center points"
    - "pfld a practical facial landmark detector"
    - "dsfd dual shot face detector"
    - "look at boundary a boundary aware face alignment algorithm"
    - "cornernet detecting objects as paired keypoints"
    - "disentangling 3d pose in a dendritic cnn for unconstrained 2d face alignment"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "gradient harmonized single stage detector"

Connected through hyperedge HE-3 (induced by source paper "1st place solutions for openimage2019 object detection and instance segmentation"):
  with members:
    - "revisiting the sibling head in object detector"
    - "detecting 11k classes large scale object detection without fine grained bounding boxes"
    - "double head rcnn rethinking classification and localization for object detection"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "cornernet detecting objects as paired keypoints"
    - "gradient harmonized single stage detector"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-4 (induced by source paper "retinaface single stage dense face localisation in the wild"):
  with members:
    - "robust and high performance face detector"
    - "on learning 3d face morphable model from in the wild images"
    - "wider face and pedestrian challenge 2018 methods and results"
    - "improved selective refinement network for face detection"
    - "dense 3d face decoding over 2500fps joint texture shape convolutional mesh decoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-5 (induced by source paper "refineface refinement neural network for high performance face detection"):
  with members:
    - "robust and high performance face detector"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "pyramidbox high performance detector for finding tiny face"
    - "retinaface single stage dense face localisation in the wild"
    - "accurate face detection for high performance"
    - "improved selective refinement network for face detection"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"

Connected through hyperedge HE-6 (induced by source paper "dafe fd density aware feature enrichment for face detection"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "search to distill pearls are everywhere but not the eyes"):
  with members:
    - "knowledge distillation via route constrained optimization"
    - "towards flops constrained face recognition"
    - "searching for mobilenetv3"
    - "deep high resolution representation learning for visual recognition"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "exploring disentangled feature representation beyond face identification"

Connected through hyperedge HE-8 (induced by source paper "adacos adaptively scaling cosine logits for effectively learning deep face representations"):
  with members:
    - "crystal loss and quality pooling for unconstrained face verification and recognition"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "ring loss convex feature normalization for face recognition"
    - "additive margin softmax for face verification"
    - "learning deep features via congenerous cosine loss for person recognition"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"

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)