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: "deep multi instance networks with sparse label assignment for whole mammogram classification"
  Abstract: Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods rely on regions of interest (ROIs) which require great efforts to annotate. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning (MIL) for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned ROIs. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed networks compared to previous work using segmentation and detection annotations.

Connected through hyperedge HE-1 (induced by source paper "an interpretable classifier for high resolution breast cancer screening images utilizing weakly supervised localization"):
  with members:
    - "deep neural networks improve radiologists performance in breast cancer screening"
    - "processing megapixel images with deep attention sampling models"
    - "pseudoedgenet nuclei segmentation only with point annotations"
    - "constrained cnn losses for weakly supervised segmentation"
    - "learning from suspected target bootstrapping performance for breast cancer detection in mammography"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in x ray images"
    - "mammo a deep learning solution for facilitating radiologist machine collaboration in breast cancer diagnosis"

Connected through hyperedge HE-2 (induced by source paper "learning from web data with self organizing memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "c mil continuation multiple instance learning for weakly supervised object detection"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "learning from web data the benefit of unsupervised object localization"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "joint optimization framework for learning with noisy labels"

Connected through hyperedge HE-3 (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"
    - "optimizing region selection for weakly supervised object detection"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "localization of critical findings in chest x ray without local annotations using multi instance learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "classifying and segmenting microscopy images with deep multiple instance learning"
    - "you only look once unified real time object detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "the graphnet zoo a plug and play framework for deep semi supervised classification"):
  with members:
    - "two cycle learning clustering based regularisation for deep semi supervised classification"
    - "graphx net chest x ray classification under extreme minimal supervision"
    - "weakly supervised deep learning for thoracic disease classification and localization on chest x rays"
    - "comparison of deep learning approaches for multi label chest x ray classification"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "end to end training for whole image breast cancer diagnosis using an all convolutional design"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "adaptation of a deep learning malignancy model from full field digital mammography to digital breast tomosynthesis"):
  with members:
    - "spottune transfer learning through adaptive fine tuning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "learning from web data with memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning from noisy web data with category level supervision"
    - "learning to learn from noisy labeled data"

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)