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: "deep feature pyramid reconfiguration for object detection"
  Abstract: State-of-the-art object detectors usually learn multi-scale representations to get better results by employing feature pyramids. However, the current designs for feature pyramids are still inefficient to integrate the semantic information over different scales. In this paper, we begin by investigating current feature pyramids solutions, and then reformulate the feature pyramid construction as the feature reconfiguration process. Finally, we propose a novel reconfiguration architecture to combine low-level representations with high-level semantic features in a highly-nonlinear yet efficient way. In particular, our architecture which consists of global attention and local reconfigurations, is able to gather task-oriented features across different spatial locations and scales, globally and locally. Both the global attention and local reconfiguration are lightweight, in-place, and end-to-end trainable. Using this method in the basic SSD system, our models achieve consistent and significant boosts compared with the original model and its other variations, without losing real-time processing speed.

Connected through hyperedge HE-1 (induced by source paper "bridging the gap between anchor based and anchor free detection via adaptive training sample selection"):
  with members:
    - "reppoints point set representation for object detection"
    - "foveabox beyond anchor based object detector"
    - "libra r cnn towards balanced learning for object detection"
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "high level semantic feature detection a new perspective for pedestrian detection"
    - "towards accurate one stage object detection with ap loss"

Connected through hyperedge HE-2 (induced by source paper "efficient neural architecture transformation searchin channel level for object detection"):
  with members:
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "rethinking imagenet pre training"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "darts differentiable architecture search"

Connected through hyperedge HE-3 (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-4 (induced by source paper "high level semantic feature detection a new perspective for pedestrian detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "citypersons a diverse dataset for pedestrian detection"

Connected through hyperedge HE-5 (induced by source paper "side aware boundary localization for more precise object detection"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "switchable whitening for deep representation learning"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "detnas backbone search for object detection"
    - "nas fcos fast neural architecture search for object detection"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-6 (induced by source paper "carafe content aware reassembly of features"):
  with members:
    - "deep flow guided video inpainting"
    - "libra r cnn towards balanced learning for object detection"
    - "region proposal by guided anchoring"
    - "meta sr a magnification arbitrary network for super resolution"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "foreground aware image inpainting"
    - "mask scoring r cnn"
    - "guided upsampling network for real time semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "foveabox beyond anchor based object detector"):
  with members:
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "cornernet detecting objects as paired keypoints"
    - "autofocus efficient multi scale inference"
    - "metaanchor learning to detect objects with customized anchors"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-8 (induced by source paper "center and scale prediction a box free approach for pedestrian and face detection"):
  with members:
    - "dsfd dual shot face detector"
    - "cornernet detecting objects as paired keypoints"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "deep retinex decomposition for low light enhancement"
    - "focal loss for dense object detection"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

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)