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: "soft nms improving object detection with one line of code"
  Abstract: Non-maximum suppression is an integral part of the object detection pipeline. First, it sorts all detection boxes on the basis of their scores. The detection box M with the maximum score is selected and all other detection boxes with a significant overlap (using a pre-defined threshold) with M are suppressed. This process is recursively applied on the remaining boxes. As per the design of the algorithm, if an object lies within the predefined overlap threshold, it leads to a miss. To this end, we propose Soft-NMS, an algorithm which decays the detection scores of all other objects as a continuous function of their overlap with M. Hence, no object is eliminated in this process. Soft-NMS obtains consistent improvements for the coco-style mAP metric on standard datasets like PASCAL VOC 2007 (1.7% for both R-FCN and Faster-RCNN) and MS-COCO (1.3% for R-FCN and 1.1% for Faster-RCNN) by just changing the NMS algorithm without any additional hyper-parameters. Using Deformable-RFCN, Soft-NMS improves state-of-the-art in object detection from 39.8% to 40.9% with a single model. Further, the computational complexity of Soft-NMS is the same as traditional NMS and hence it can be efficiently implemented. Since Soft-NMS does not require any extra training and is simple to implement, it can be easily integrated into any object detection pipeline. Code for Soft-NMS is publicly available on GitHub (this http URL).

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 "har net joint learning of hybrid attention for single stage object detection"):
  with members:
    - "deep ordinal hashing with spatial attention"
    - "feature selective anchor free module for single shot object detection"
    - "sharp attention network via adaptive sampling for person re identification"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "gradient harmonized single stage detector"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "say as you wish fine grained control of image caption generation with abstract scene graphs"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "sequential latent spaces for modeling the intention during diverse image captioning"
    - "ctrl a conditional transformer language model for controllable generation"
    - "robust change captioning"
    - "describing like humans on diversity in image captioning"
    - "nocaps novel object captioning at scale"
    - "intention oriented image captions with guiding objects"
    - "neural baby talk"

Connected through hyperedge HE-4 (induced by source paper "cpm r cnn calibrating point guided misalignment in object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "grid r cnn plus faster and better"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "mask scoring r cnn"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"

Connected through hyperedge HE-5 (induced by source paper "featurenms non maximum suppression by learning feature embeddings"):
  with members:
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "yolov3 an incremental improvement"
    - "crowdhuman a benchmark for detecting human in a crowd"
    - "sampling matters in deep embedding learning"
    - "deep learning based large scale visual recommendation and search for e commerce"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "constraining temporal relationship for action localization"):
  with members:
    - "scale matters temporal scale aggregation network for precise action localization in untrimmed videos"
    - "graph convolutional networks for temporal action localization"
    - "cornernet detecting objects as paired keypoints"
    - "moments in time dataset one million videos for event understanding"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-7 (induced by source paper "matrixnets a new scale and aspect ratio aware architecture for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"
    - "cornernet lite efficient keypoint based object detection"
    - "freeanchor learning to match anchors for visual object detection"
    - "cornernet detecting objects as paired keypoints"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "accurate temporal action proposal generation with relation aware pyramid network"):
  with members:
    - "long term feature banks for detailed video understanding"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "multi granularity generator for temporal action proposal"
    - "ctap complementary temporal action proposal generation"
    - "reconstruction network for video captioning"
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"

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)