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: "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
  Abstract: We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.

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 "sam the sensitivity of attribution methods to hyperparameters"):
  with members:
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "towards explainable artificial intelligence"
    - "visualizing deep networks by optimizing with integrated gradients"
    - "removing input features via a generative model to explain their attributions to classifier s decisions"
    - "methods for interpreting and understanding deep neural networks"
    - "understanding deep architectures by visual summaries"
    - "interpreting and explaining deep neural networks for classification of audio signals"
    - "explaining image classifiers by counterfactual generation"

Connected through hyperedge HE-3 (induced by source paper "brain metastasis segmentation network trained with robustness to annotations with multiple false negatives"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "identifying the best machine learning algorithms for brain tumor segmentation progression assessment and overall survival prediction in the brats challenge"
    - "focal loss for dense object detection"
    - "learning from weak and noisy labels for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "an adversarial approach for the robust classification of pneumonia from chest radiographs"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning explainable models using attribution priors"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "transfusion understanding transfer learning for medical imaging"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "deep learning predicts hip fracture using confounding patient and healthcare variables"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"

Connected through hyperedge HE-5 (induced by source paper "dynamic graph correlation learning for disease diagnosis with incomplete labels"):
  with members:
    - "learning a deep convnet for multi label classification with partial labels"
    - "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"
    - "multi label image recognition with graph convolutional networks"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-6 (induced by source paper "chexclusion fairness gaps in deep chest x ray classifiers"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "a simulation based dynamic evaluation framework for system wide algorithmic fairness"
    - "on the fairness of disentangled representations"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-7 (induced by source paper "neuroevolution of neural network architectures using codeepneat and keras"):
  with members:
    - "evolutionary neural automl for deep learning"
    - "autoaugment learning augmentation policies from data"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "a survey on deep learning in medical image analysis"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "microsoft coco captions data collection and evaluation server"

Connected through hyperedge HE-8 (induced by source paper "continual learning for domain adaptation in chest x ray classification"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "a strong baseline for domain adaptation and generalization in medical imaging"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "gradient episodic memory for continual learning"
    - "overcoming catastrophic forgetting in neural networks"
    - "densely connected convolutional networks"
    - "learning without forgetting"

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)