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: "solution for large scale hierarchical object detection datasets with incomplete annotation and data imbalance"
  Abstract: This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical features: large-scale, hierarchical tag system, severe annotation incompleteness and data imbalance. Considering these characteristics, an amount of strategies are employed, including SNIPER, soft sampling, class-aware sampling (CAS), hierarchical non-maximum suppression (HNMS) and so on. In virtue of these effective strategies, and further using the powerful SENet154 armed with feature pyramid module and deformable ROIalign as the backbone, our best single model could achieve a mAP of 56.9%. After a further ensemble with 9 models, the final mAP is boosted to 62.2% in the public leaderboard (ranked the 2nd place) and 58.6% in the private leaderboard (ranked the 3rd place, slightly inferior to the 1st place by only 0.04 point).

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning an efficient network for large scale hierarchical object detection with data imbalance 3rd place solution to open images challenge 2019"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "large scale interactive object segmentation with human annotators"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"
    - "rethinking imagenet pre training"
    - "sampling techniques for large scale object detection from sparsely annotated objects"
    - "yolov3 an incremental improvement"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"

Connected through hyperedge HE-3 (induced by source paper "sampling techniques for large scale object detection from sparsely annotated objects"):
  with members:
    - "part aware fine grained object categorization using weakly supervised part detection network"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "soft sampling for robust object detection"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "transfer learning by ranking for weakly supervised object annotation"
    - "squeeze and excitation networks"
    - "chainercv a library for deep learning in computer vision"

Connected through hyperedge HE-4 (induced by source paper "soft sampling for robust object detection"):
  with members:
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "path aggregation network for instance segmentation"
    - "soft nms improving object detection with one line of code"
    - "focal loss for dense object detection"
    - "deformable convolutional networks"
    - "megdet a large mini batch object detector"
    - "an analysis of scale invariance in object detection snip"
    - "r fcn 3000 at 30fps decoupling detection and classification"

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)