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: "self transfer learning for fully weakly supervised object localization"
  Abstract: Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data/task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches.

Connected through hyperedge HE-1 (induced by source paper "deep adaptive learning for writer identification based on single handwritten word images"):
  with members:
    - "an overview of multi task learning in deep neural networks"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "cross stitch networks for multi task learning"
    - "deepwriter a multi stream deep cnn for text independent writer identification"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial complementary learning for weakly supervised object localization"):
  with members:
    - "few example object detection with model communication"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "deep self taught learning for weakly supervised object localization"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "two phase learning for weakly supervised object localization"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "dual path networks"
    - "discovering class specific pixels for weakly supervised semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised localization and classification of proximal femur fractures"):
  with members:
    - "compression fractures detection on ct"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"
    - "deep residual learning for image recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "deep temporal clustering fully unsupervised learning of time domain features"):
  with members:
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "deep learning in neural networks"

Connected through hyperedge HE-6 (induced by source paper "thoracic disease identification and localization with limited supervision"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "attention correctness in neural image captioning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for 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)