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 object detection using deep neural networks"
  Abstract: Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.

Connected through hyperedge HE-1 (induced by source paper "real time object detection and recognition on low compute humanoid robots using deep learning"):
  with members:
    - "yolov3 an incremental improvement"
    - "using convolutional neural networks in robots with limited computational resources detecting nao robots while playing soccer"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "r fcn object detection via region based fully convolutional networks"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "extd extremely tiny face detector via iterative filter reuse"):
  with members:
    - "depthwise convolution is all you need for learning multiple visual domains"
    - "thundernet towards real time generic object detection"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "improved selective refinement network for face detection"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "dsfd dual shot face detector"
    - "tiny dsod lightweight object detection for resource restricted usages"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "reppoints point set representation for object detection"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "region proposal by guided anchoring"
    - "feature selective anchor free module for single shot object detection"
    - "consistent optimization for single shot object detection"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "group normalization"
    - "metaanchor learning to detect objects with customized anchors"

Connected through hyperedge HE-4 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-5 (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-6 (induced by source paper "shape aware feature extraction for instance segmentation"):
  with members:
    - "polarmask single shot instance segmentation with polar representation"
    - "mask scoring r cnn"
    - "tensormask a foundation for dense object segmentation"
    - "yolact real time instance segmentation"
    - "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"
    - "bottom up object detection by grouping extreme and center points"
    - "path aggregation network for instance segmentation"
    - "segmentation aware convolutional networks using local attention masks"

Connected through hyperedge HE-7 (induced by source paper "you only look listen once towards fast and accurate visual grounding"):
  with members:
    - "mattnet modular attention network for referring expression comprehension"
    - "lstm a search space odyssey"
    - "non local neural networks"
    - "grounding referring expressions in images by variational context"
    - "attention is all you need"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "retinamask learning to predict masks improves state of the art single shot detection for free"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "yolov3 an incremental improvement"
    - "path aggregation network for instance segmentation"
    - "densepose dense human pose estimation in the wild"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "soft nms improving object detection with one line of code"
    - "learning transferable architectures for scalable image recognition"

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)