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: "how good are detection proposals really"
  Abstract: Current top performing Pascal VOC object detectors employ detection proposals to guide the search for objects thereby avoiding exhaustive sliding window search across images. Despite the popularity of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in depth analysis of ten object proposal methods along with four baselines regarding ground truth annotation recall (on Pascal VOC 2007 and ImageNet 2013), repeatability, and impact on DPM detector performance. Our findings show common weaknesses of existing methods, and provide insights to choose the most adequate method for different settings.

Connected through hyperedge HE-1 (induced by source paper "deep neural networks for marine debris detection in sonar images"):
  with members:
    - "on the convergence of adam and beyond"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "learning transferable architectures for scalable image recognition"
    - "dsod learning deeply supervised object detectors from scratch"

Connected through hyperedge HE-2 (induced by source paper "cascade rpn delving into high quality region proposal network with adaptive convolution"):
  with members:
    - "region proposal by guided anchoring"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "feature selective anchor free module for single shot object detection"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "cornernet detecting objects as paired keypoints"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "wordfence text detection in natural images with border awareness"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"

Connected through hyperedge HE-4 (induced by source paper "large scale object mining for object discovery from unlabeled video"):
  with members:
    - "unsupervised hard example mining from videos for improved object detection"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "in defense of the triplet loss for person re identification"
    - "object category learning and retrieval with weak supervision"
    - "track then decide category agnostic vision based multi object tracking"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-5 (induced by source paper "omnidrl robust pedestrian detection using deep reinforcement learning on omnidirectional cameras"):
  with members:
    - "yolov3 an incremental improvement"
    - "hydraplus net attentive deep features for pedestrian analysis"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "efficient and robust pedestrian detection using deep learning for human aware navigation"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-6 (induced by source paper "semantic hierarchical priors for intrinsic image decomposition"):
  with members:
    - "deep hybrid real and synthetic training for intrinsic decomposition"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "shading annotations in the wild"
    - "reflectance adaptive filtering improves intrinsic image estimation"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning objectness from sonar images for class independent object detection"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "real time convolutional networks for sonar image classification in low power embedded systems"
    - "what makes for effective detection proposals"
    - "objectness scoring and detection proposals in forward looking sonar images with convolutional neural networks"
    - "object proposal evaluation protocol is gameable"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "towards pedestrian detection using retinanet in eccv 2018 wider pedestrian detection challenge"):
  with members:
    - "scale aware fast r cnn for pedestrian detection"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)