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: "optimizing region selection for weakly supervised object detection"
  Abstract: Training object detectors with only image-level annotations is very challenging because the target objects are often surrounded by a large number of background clutters. Many existing approaches tackle this problem through object proposal mining. However, the collected positive regions are either low in precision or lack of diversity, and the strategy of collecting negative regions is not carefully designed, neither. Moreover, training is often slow because region selection and object detector training are processed separately. In this context, the primary contribution of this work is to improve weakly supervised detection with an optimized region selection strategy. The proposed method collects purified positive training regions by progressively removing easy background clutters, and selects discriminative negative regions by mining class-specific hard samples. This region selection procedure is further integrated into a CNN-based weakly supervised detection (WSD) framework, and can be performed in each stochastic gradient descent mini-batch during training. Therefore, the entire model can be trained end-to-end efficiently. Extensive evaluation results on PASCAL VOC 2007, VOC 2010 and VOC 2012 datasets are presented which demonstrate that the proposed method effectively improves WSD.

Connected through hyperedge HE-1 (induced by source paper "weakly and semi supervised detection in medical imaging via deep dual branch net"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "who said what modeling individual labelers improves classification"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"

Connected through hyperedge HE-2 (induced by source paper "a dual branch deep neural network for classification and detection in mammograms"):
  with members:
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "weakly supervised object localization with multi fold multiple instance learning"
    - "weakly and semi supervised object detection with expectation maximization algorithm"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "weakly supervised deep detection networks"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"

Connected through hyperedge HE-3 (induced by source paper "classification and detection in mammograms with weak supervision via dual branch deep neural net"):
  with members:
    - "a multi scale cnn and curriculum learning strategy for mammogram classification"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "weakly supervised deep detection networks"
    - "return of the devil in the details delving deep into convolutional nets"

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)