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: "single shot object detection with enriched semantics"
  Abstract: We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a location-agnostic module. The segmentation branch is supervised by weak segmentation ground-truth, i.e., no extra annotation is required. In conjunction with that, we employ a location-agnostic module which learns relationship between channels and object classes in a self-supervised manner. Comprehensive experimental results on both PASCAL VOC and MS COCO detection datasets demonstrate the effectiveness of the proposed method. In particular, with a VGG16 based DES, we achieve an mAP of 81.6 on VOC2007 test and an mmAP of 32.8 on COCO test-dev with an inference speed of 36.7 milliseconds per image on a Titan X Pascal GPU. With a lower resolution version, we achieve an mAP of 79.5 on VOC2007 with an inference speed of 14.7 milliseconds per image.

Connected through hyperedge HE-1 (induced by source paper "proposal learning for semi supervised object detection"):
  with members:
    - "momentum contrast for unsupervised visual representation learning"
    - "deep high resolution representation learning for visual recognition"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "mask scoring r cnn"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "unsupervised data augmentation for consistency training"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cornernet lite efficient keypoint based object detection"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "autofocus efficient multi scale inference"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "acquisition of localization confidence for accurate object detection"

Connected through hyperedge HE-4 (induced by source paper "autoslim towards one shot architecture search for channel numbers"):
  with members:
    - "universally slimmable networks and improved training techniques"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "learning to prune filters in convolutional neural networks"
    - "slimmable neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "graph hypernetworks for neural architecture search"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-5 (induced by source paper "improved selective refinement network for face detection"):
  with members:
    - "robust and high performance face detector"
    - "rethinking imagenet pre training"
    - "dsfd dual shot face detector"
    - "face detection using improved faster rcnn"
    - "group normalization"
    - "fa rpn floating region proposals for face detection"
    - "real time rotation invariant face detection with progressive calibration networks"
    - "pyramidbox a context assisted single shot face detector"

Connected through hyperedge HE-6 (induced by source paper "dafe fd density aware feature enrichment for face detection"):
  with members:
    - "face magnet magnifying feature maps to detect small faces"
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "pushing the limits of unconstrained face detection a challenge dataset and baseline results"
    - "switching convolutional neural network for crowd counting"
    - "generating high quality crowd density maps using contextual pyramid cnns"
    - "cms rcnn contextual multi scale region based cnn for unconstrained face detection"
    - "illuminating pedestrians via simultaneous detection segmentation"
    - "high quality facial photo sketch synthesis using multi adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "spatialflow bridging all tasks for panoptic segmentation"):
  with members:
    - "panoptic segmentation"
    - "retinamask learning to predict masks improves state of the art single shot detection for free"
    - "an end to end network for panoptic segmentation"
    - "region proposal by guided anchoring"
    - "upsnet a unified panoptic segmentation network"
    - "box driven class wise region masking and filling rate guided loss for weakly supervised semantic segmentation"
    - "deeperlab single shot image parser"
    - "mmdetection open mmlab detection toolbox and benchmark"

Connected through hyperedge HE-8 (induced by source paper "weight standardization"):
  with members:
    - "ssn learning sparse switchable normalization via sparsestmax"
    - "a mean field theory of batch normalization"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "differentiable learning to normalize via switchable normalization"
    - "deep co training for semi supervised image recognition"
    - "dynamic graph cnn for learning on point clouds"
    - "towards understanding regularization in batch normalization"

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)