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: "bach grand challenge on breast cancer histology images"
  Abstract: Abstract   Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology.

Connected through hyperedge HE-1 (induced by source paper "a question centric model for visual question answering in medical imaging"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "concept centric visual turing tests for method validation"
    - "overcoming data limitation in medical visual question answering"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "visual question answering datasets algorithms and future challenges"
    - "mutan multimodal tucker fusion for visual question answering"

Connected through hyperedge HE-2 (induced by source paper "breast cancer histopathology image classification and localization using multiple instance learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based deep multiple instance learning"
    - "classification of breast cancer histology using deep learning"
    - "deep residual learning for image recognition"
    - "accelerating very deep convolutional networks for classification and detection"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-3 (induced by source paper "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "data efficient image recognition with contrastive predictive coding"
    - "cluster based learning from weakly labeled bags in digital pathology"
    - "her2 challenge contest a detailed assessment of automated her2 scoring algorithms in whole slide images of breast cancer tissues"
    - "attention based deep multiple instance learning"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "classification of breast cancer histology using deep learning"
    - "smooth loss functions for deep top k classification"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "learning with less data via weakly labeled patch classification in digital pathology"):
  with members:
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "improving prostate cancer detection with breast histopathology images"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "rotation equivariant cnns for digital pathology"
    - "sampling matters in deep embedding learning"
    - "classification and retrieval of digital pathology scans a new dataset"
    - "no fuss distance metric learning using proxies"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "o medal online active deep learning for medical image analysis"):
  with members:
    - "cost effective active learning for melanoma segmentation"
    - "a survey on deep learning in medical image analysis"
    - "deep bayesian active learning with image data"
    - "understanding deep learning requires rethinking generalization"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "rethinking the inception architecture for computer vision"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "weakly supervised prostate tma classification via graph convolutional networks"):
  with members:
    - "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"
    - "self attention graph pooling"
    - "deep adversarial training for multi organ nuclei segmentation in histopathology images"
    - "representation learning with contrastive predictive coding"
    - "semi supervised classification with graph convolutional networks"
    - "computational pathology challenges and promises for tissue analysis"

Connected through hyperedge HE-7 (induced by source paper "multi layer domain adaptation for deep convolutional networks"):
  with members:
    - "training medical image analysis systems like radiologists"
    - "semi supervised deep learning for fully convolutional networks"
    - "asymmetric tri training for unsupervised domain adaptation"
    - "squeeze and excitation networks"
    - "improved training of wasserstein gans"

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)