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: "image co localization by mimicking a good detector s confidence score distribution"
  Abstract: Given a set of images containing objects from the same category, the task of image co-localization is to identify and localize each instance. This paper shows that this problem can be solved by a simple but intriguing idea, that is, a common object detector can be learnt by making its detection confidence scores distributed like those of a strongly supervised detector. More specifically, we observe that given a set of object proposals extracted from an image that contains the object of interest, an accurate strongly supervised object detector should give high scores to only a small minority of proposals, and low scores to most of them. Thus, we devise an entropy-based objective function to enforce the above property when learning the common object detector. Once the detector is learnt, we resort to a segmentation approach to refine the localization. We show that despite its simplicity, our approach outperforms state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "universal bounding box regression and its applications"):
  with members:
    - "soft nms improving object detection with one line of code"
    - "multiple instance detection network with online instance classifier refinement"
    - "mask r cnn"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning to refine object segments"

Connected through hyperedge HE-2 (induced by source paper "unsupervised image matching and object discovery as optimization"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mask r cnn"
    - "unsupervised learning of visual representations by solving jigsaw puzzles"
    - "unsupervised visual representation learning by context prediction"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "unsupervised object discovery and tracking in video collections"

Connected through hyperedge HE-3 (induced by source paper "deep feature factorization for concept discovery"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "co localization with category consistent features and geodesic distance propagation"
    - "why should i trust you explaining the predictions of any classifier"
    - "do semantic parts emerge in convolutional neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "zigzag learning for weakly supervised object detection"):
  with members:
    - "how hard can it be estimating the difficulty of visual search in an image"
    - "deep self taught learning for weakly supervised object localization"
    - "multiple instance detection network with online instance classifier refinement"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "weakly supervised object localization using size estimates"
    - "you only look once unified real time object detection"
    - "weakly supervised deep detection networks"

Connected through hyperedge HE-5 (induced by source paper "co localization with category consistent features and geodesic distance propagation"):
  with members:
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "bayesian joint topic modelling for weakly supervised object localisation"
    - "ssd single shot multibox detector"
    - "weakly supervised localization using deep feature maps"
    - "geodesic distance histogram feature for video segmentation"
    - "you only look once unified real time object detection"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "relaxed multiple instance svm with application to object discovery"

Connected through hyperedge HE-6 (induced by source paper "ensemble of part detectors for simultaneous classification and localization"):
  with members:
    - "hcp a flexible cnn framework for multi label image classification"
    - "weakly supervised deep detection networks"
    - "mid level deep pattern mining"
    - "very deep convolutional networks for large scale image recognition"
    - "automatic discovery and optimization of parts for image classification"
    - "caffe convolutional architecture for fast feature embedding"
    - "multi scale orderless pooling of deep convolutional activation features"
    - "visualizing and understanding convolutional networks"

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)