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 sampling for robust object detection"
  Abstract: We study the robustness of object detection under the presence of missing annotations. In this setting, the unlabeled object instances will be treated as background, which will generate an incorrect training signal for the detector. Interestingly, we observe that after dropping 30% of the annotations (and labeling them as background), the performance of CNN-based object detectors like Faster-RCNN only drops by 5% on the PASCAL VOC dataset. We provide a detailed explanation for this result. To further bridge the performance gap, we propose a simple yet effective solution, called Soft Sampling. Soft Sampling re-weights the gradients of RoIs as a function of overlap with positive instances. This ensures that the uncertain background regions are given a smaller weight compared to the hardnegatives. Extensive experiments on curated PASCAL VOC datasets demonstrate the effectiveness of the proposed Soft Sampling method at different annotation drop rates. Finally, we show that on OpenImagesV3, which is a real-world dataset with missing annotations, Soft Sampling outperforms standard detection baselines by over 3%.

Connected through hyperedge HE-1 (induced by source paper "object detection as a positive unlabeled problem"):
  with members:
    - "an empirical study of example forgetting during deep neural network learning"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "omnia faster r cnn detection in the wild through dataset merging and soft distillation"
    - "positive unlabeled learning with non negative risk estimator"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "ava a video dataset of spatio temporally localized atomic visual actions"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"

Connected through hyperedge HE-2 (induced by source paper "lesion harvester iteratively mining unlabeled lesions and hard negative examples at scale"):
  with members:
    - "attentive ct lesion detection using deep pyramid inference with multi scale booster"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"
    - "objects as points"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "focal loss for dense object detection"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-3 (induced by source paper "solving missing annotation object detection with background recalibration loss"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "multimodal unsupervised image to image translation"
    - "gradient harmonized single stage detector"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "focal loss for dense object detection"
    - "dsod learning deeply supervised object detectors from scratch"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "faster ilod incremental learning for object detectors based on faster rcnn"):
  with members:
    - "rilod near real time incremental learning for object detection at the edge"
    - "large scale incremental learning"
    - "m2kd multi model and multi level knowledge distillation for incremental learning"
    - "end to end incremental learning"
    - "focal loss for dense object detection"
    - "incremental learning of object detectors without catastrophic forgetting"
    - "icarl incremental classifier and representation learning"
    - "learning without forgetting"

Connected through hyperedge HE-5 (induced by source paper "detecting 11k classes large scale object detection without fine grained bounding boxes"):
  with members:
    - "yolov3 an incremental improvement"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "sniper efficient multi scale training"
    - "exploiting web images for weakly supervised object detection"
    - "soft nms improving object detection with one line of code"
    - "zero annotation object detection with web knowledge transfer"
    - "extreme clicking for efficient object annotation"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-6 (induced by source paper "distant vehicle detection using radar and vision"):
  with members:
    - "deep learning is robust to massive label noise"
    - "object detection on dynamic occupancy grid maps using deep learning and automatic label generation"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "faster bounding box annotation for object detection in indoor scenes"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense object detection"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-7 (induced by source paper "training object detectors with noisy data"):
  with members:
    - "distant vehicle detection using radar and vision"
    - "unsupervised hard example mining from videos for improved object detection"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "exploring the limits of weakly supervised pretraining"
    - "learning to reweight examples for robust deep learning"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "detecting traffic lights by single shot detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "omnia faster r cnn detection in the wild through dataset merging and soft distillation"):
  with members:
    - "zero shot detection"
    - "modanet a large scale street fashion dataset with polygon annotations"
    - "cornernet detecting objects as paired keypoints"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "combining heterogeneously labeled datasets for training segmentation networks"
    - "reducing overconfident errors outside the known distribution"
    - "open set domain adaptation by backpropagation"
    - "a two stream mutual attention network for semi supervised biomedical segmentation with noisy labels"

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)