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: "autofocus efficient multi scale inference"
  Abstract: This paper describes AutoFocus, an efficient multi-scale inference algorithm for deep-learning based object detectors. Instead of processing an entire image pyramid, AutoFocus adopts a coarse to fine approach and only processes regions which are likely to contain small objects at finer scales. This is achieved by predicting category agnostic segmentation maps for small objects at coarser scales, called FocusPixels. FocusPixels can be predicted with high recall, and in many cases, they only cover a small fraction of the entire image. To make efficient use of FocusPixels, an algorithm is proposed which generates compact rectangular FocusChips which enclose FocusPixels. The detector is only applied inside FocusChips, which reduces computation while processing finer scales. Different types of error can arise when detections from FocusChips of multiple scales are combined, hence techniques to correct them are proposed. AutoFocus obtains an mAP of 47.9% (68.3% at 50% overlap) on the COCO test-dev set while processing 6.4 images per second on a Titan X (Pascal) GPU. This is 2.5X faster than our multi-scale baseline detector and matches its mAP. The number of pixels processed in the pyramid can be reduced by 5X with a 1% drop in mAP. AutoFocus obtains more than 10% mAP gain compared to RetinaNet but runs at the same speed with the same ResNet-101 backbone.

Connected through hyperedge HE-1 (induced by source paper "panda a gigapixel level human centric video dataset"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "cvpr19 tracking and detection challenge how crowded can it get"
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "widerperson a diverse dataset for dense pedestrian detection in the wild"
    - "group reidentification with multigrained matching and integration"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "vision meets drones a challenge"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"

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"
    - "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"
    - "deformable convnets v2 more deformable better results"

Connected through hyperedge HE-4 (induced by source paper "autoscale learning to scale for crowd counting"):
  with members:
    - "ha ccn hierarchical attention based crowd counting network"
    - "exploiting unlabeled data in cnns by self supervised learning to rank"
    - "point in box out beyond counting persons in crowds"
    - "learning from synthetic data for crowd counting in the wild"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "composition loss for counting density map estimation and localization in dense crowds"
    - "where are the blobs counting by localization with point supervision"
    - "context aware crowd counting"

Connected through hyperedge HE-5 (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"
    - "metaanchor learning to detect objects with customized anchors"
    - "deep learning for generic object detection a survey"
    - "yolov3 an incremental improvement"
    - "deep feature pyramid reconfiguration for object detection"

Connected through hyperedge HE-6 (induced by source paper "liteeval a coarse to fine framework for resource efficient video recognition"):
  with members:
    - "scsampler sampling salient clips from video for efficient action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "adaframe adaptive frame selection for fast video recognition"
    - "sniper efficient multi scale training"
    - "slowfast networks for video recognition"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "it s all about the scale efficient text detection using adaptive scaling"):
  with members:
    - "character region awareness for text detection"
    - "adascale towards real time video object detection using adaptive scaling"
    - "pyramid mask text detector"
    - "shape robust text detection with progressive scale expansion network"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-8 (induced by source paper "pyramidbox high performance detector for finding tiny face"):
  with members:
    - "region proposal by guided anchoring"
    - "mask scoring r cnn"
    - "dsfd dual shot face detector"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "metaanchor learning to detect objects with customized anchors"
    - "seeing small faces from robust anchor s perspective"
    - "pyramidbox a context assisted single shot face detector"
    - "acquisition of localization confidence for accurate 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)