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: "stairnet top down semantic aggregation for accurate one shot detection"
  Abstract: One-stage object detectors such as SSD or YOLO already have shown promising accuracy with small memory footprint and fast speed. However, it is widely recognized that one-stage detectors have difficulty in detecting small objects while they are competitive with two-stage methods on large objects. In this paper, we investigate how to alleviate this problem starting from the SSD framework. Due to their pyramidal design, the lower layer that is responsible for small objects lacks strong semantics(e.g contextual information). We address this problem by introducing a feature combining module that spreads out the strong semantics in a top-down manner. Our final model StairNet detector unifies the multi-scale representations and semantic distribution effectively. Experiments on PASCAL VOC 2007 and PASCAL VOC 2012 datasets demonstrate that StairNet significantly improves the weakness of SSD and outperforms the other state-of-the-art one-stage detectors.

Connected through hyperedge HE-1 (induced by source paper "fast perception planning and execution for a robotic butler wheeled humanoid m hubo"):
  with members:
    - "harmonious sampling for mobile manipulation planning"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "deep sliding shapes for amodal 3d object detection in rgb d images"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "learning spatial fusion for single shot object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "adaptively connected neural networks"
    - "bag of freebies for training object detection neural networks"
    - "feature selective anchor free module for single shot object detection"
    - "fcos fully convolutional one stage object detection"
    - "scale aware trident networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "nas fpn learning scalable feature pyramid architecture for object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning short cut connections for object counting"
    - "dropblock a regularization method for convolutional networks"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "detnet a backbone network for object detection"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "scarfnet multi scale features with deeply fused and redistributed semantics for enhanced object detection"):
  with members:
    - "deep learning for generic object detection a survey"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"
    - "mask r cnn"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-5 (induced by source paper "deep feature pyramid reconfiguration for object detection"):
  with members:
    - "path aggregation network for instance segmentation"
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "couplenet coupling global structure with local parts for object detection"
    - "focal loss for dense object detection"
    - "learning feature pyramids for human pose estimation"
    - "dssd deconvolutional single shot detector"
    - "single shot refinement neural network for object detection"
    - "squeeze and excitation networks"

Connected through hyperedge HE-6 (induced by source paper "cbam convolutional block attention module"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "bam bottleneck attention module"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "residual attention network for image classification"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-7 (induced by source paper "how to extract fashion trends from social media a robust object detector with support for unsupervised learning"):
  with members:
    - "dssd deconvolutional single shot detector"
    - "ssd single shot multibox detector"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "object detectors emerge in deep scene cnns"

Connected through hyperedge HE-8 (induced by source paper "residual features and unified prediction network for single stage detection"):
  with members:
    - "enhancement of ssd by concatenating feature maps for object detection"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "accurate single stage detector using recurrent rolling convolution"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint object detection"

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)