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: "detecting cancer metastases on gigapixel pathology images"
  Abstract: Each year, the treatment decisions for more than 230,000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x 100,000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Camelyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.

Connected through hyperedge HE-1 (induced by source paper "a survey on explainable artificial intelligence xai towards medical xai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable neural networks for predicting mortality risk using multi modal electronic health records"
    - "an evaluation of the human interpretability of explanation"
    - "protoattend attention based prototypical learning"
    - "understanding the un interpretability of natural image distributions using generative models"
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "interpretable dynamics models for data efficient reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deep learning models for digital pathology"):
  with members:
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "inferring a third spatial dimension from 2d histological images"
    - "deep learning based assessment of tumor associated stroma for diagnosing breast cancer in histopathology images"
    - "context based normalization of histological stains using deep convolutional features"
    - "context aware stacked convolutional neural networks for classification of breast carcinomas in whole slide histopathology images"
    - "representation aggregation networks for segmentation of multi gigapixel histology images"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"):
  with members:
    - "efficient privacy preserving nonconvex optimization"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "laplacian smoothing gradient descent"
    - "differentially private federated learning a client level perspective"
    - "differentially private learning of undirected graphical models using collective graphical models"

Connected through hyperedge HE-4 (induced by source paper "transfusion understanding transfer learning for medical imaging"):
  with members:
    - "similarity of neural network representations revisited"
    - "investigating multilingual nmt representations at scale"
    - "the bottom up evolution of representations in the transformer a study with machine translation and language modeling objectives"
    - "rethinking imagenet pre training"
    - "direct uncertainty prediction for medical second opinions"
    - "do better imagenet models transfer better"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-5 (induced by source paper "p3sgd patient privacy preserving sgd for regularizing deep cnns in pathological image classification"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "deja vu an empirical evaluation of the memorization properties of convnets"
    - "differentially private federated learning a client level perspective"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "efficient private erm for smooth objectives"
    - "learning differentially private recurrent language models"

Connected through hyperedge HE-6 (induced by source paper "semi supervised learning for cancer detection of lymph node metastases"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "deep neural network ensembles for time series classification"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "automatic brain tumor segmentation using convolutional neural networks with test time augmentation"
    - "make nearly every neural network better generating neural network ensembles by weight parameter resampling"
    - "rotation equivariant cnns for digital pathology"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-7 (induced by source paper "processing megapixel images with deep attention sampling models"):
  with members:
    - "classification of breast cancer histology using deep learning"
    - "variational saccading efficient inference for large resolution images"
    - "two stage convolutional neural network for breast cancer histology image classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "ssd single shot multibox detector"
    - "wider or deeper revisiting the resnet model for visual recognition"
    - "tensorflow a system for large scale machine learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-8 (induced by source paper "semi supervised histology classification using deep multiple instance learning and contrastive predictive coding"):
  with members:
    - "bach grand challenge on breast cancer histology images"
    - "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"

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)