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: "scalable high quality object detection"
  Abstract: Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the proposals come unranked or with very weak ranking, making it hard to trade-off quality for running time. This raises the more fundamental question of whether high-quality proposal generation requires careful engineering or can be derived just from data alone. We demonstrate that learning-based proposal methods can effectively match the performance of hand-engineered methods while allowing for very efficient runtime-quality trade-offs. Using the multi-scale convolutional MultiBox (MSC-MultiBox) approach, we substantially advance the state-of-the-art on the ILSVRC 2014 detection challenge data set, with $0.5$ mAP for a single model and $0.52$ mAP for an ensemble of two models. MSC-Multibox significantly improves the proposal quality over its predecessor MultiBox~method: AP increases from $0.42$ to $0.53$ for the ILSVRC detection challenge. Finally, we demonstrate improved bounding-box recall compared to Multiscale Combinatorial Grouping with less proposals on the Microsoft-COCO data set.

Connected through hyperedge HE-1 (induced by source paper "towards adversarially robust object detection"):
  with members:
    - "rethinking imagenet pre training"
    - "robust adversarial perturbation on deep proposal based models"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "feature denoising for improving adversarial robustness"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "yolov3 an incremental improvement"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"

Connected through hyperedge HE-2 (induced by source paper "customizing object detectors for indoor robots"):
  with members:
    - "yolov3 an incremental improvement"
    - "intelligently assisting human guided quadcopter photography"
    - "recognizing objects in the wild where do we stand"
    - "dsod learning deeply supervised object detectors from scratch"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "distill 2md mtl data distillation based on multi dataset multi domain multi task frame work to solve face related tasksks multi task learning semi supervised learning"):
  with members:
    - "model compression via distillation and quantization"
    - "deep facial expression recognition a survey"
    - "data distillation towards omni supervised learning"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "facenet2expnet regularizing a deep face recognition net for expression recognition"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "cyclical learning rates for training neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning to learn relation for important people detection in still images"):
  with members:
    - "relational deep reinforcement learning"
    - "latent embeddings for collective activity recognition"
    - "personrank detecting important people in images"
    - "attention is all you need"
    - "relation networks for object detection"
    - "learning to compare relation network for few shot learning"
    - "massive exploration of neural machine translation architectures"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-5 (induced by source paper "exploring the ideal depth of neural network when predicting question deletion on community question answering"):
  with members:
    - "benefits of depth in neural networks"
    - "wide residual networks"
    - "on the depth of deep neural networks a theoretical view"
    - "training very deep networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "fitnets hints for thin deep nets"
    - "very deep convolutional networks for large scale image recognition"
    - "chaff from the wheat characterization and modeling of deleted questions on stack overflow"

Connected through hyperedge HE-6 (induced by source paper "improved super resolution convolution neural network for large images"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "accurate image super resolution using very deep convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face representation by joint identification verification"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning in radiology an overview of the concepts and a survey of the state of the art"):
  with members:
    - "segmentation of the proximal femur from mr images using deep convolutional neural networks"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "brain tumor segmentation with deep neural networks"
    - "an analysis of deep neural network models for practical applications"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "metaanchor learning to detect objects with customized anchors"):
  with members:
    - "detnet a backbone network for object detection"
    - "deep perm set net learn to predict sets with unknown permutation and cardinality using deep neural networks"
    - "sface an efficient network for face detection in large scale variations"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "fssd feature fusion single shot multibox detector"
    - "s 3 fd single shot scale invariant face detector"
    - "focal loss for dense object detection"
    - "what can help pedestrian 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)