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: "negbio a high performance tool for negation and uncertainty detection in radiology reports"
  Abstract: Negative and uncertain medical findings are frequent in radiology reports, but discriminating them from positive findings remains challenging for information extraction. Here, we propose a new algorithm, NegBio, to detect negative and uncertain findings in radiology reports. Unlike previous rule-based methods, NegBio utilizes patterns on universal dependencies to identify the scope of triggers that are indicative of negation or uncertainty. We evaluated NegBio on four datasets, including two public benchmarking corpora of radiology reports, a new radiology corpus that we annotated for this work, and a public corpus of general clinical texts. Evaluation on these datasets demonstrates that NegBio is highly accurate for detecting negative and uncertain findings and compares favorably to a widely-used state-of-the-art system NegEx (an average of 9.5% improvement in precision and 5.1% in F1-score).

Connected through hyperedge HE-1 (induced by source paper "when radiology report generation meets knowledge graph"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "language conditioned graph networks for relational reasoning"
    - "iterative visual reasoning beyond convolutions"
    - "on the automatic generation of medical imaging reports"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "exploring visual relationship for image captioning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "clinically accurate chest x ray report generation"):
  with members:
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "mimic cxr jpg a large publicly available database of labeled chest radiographs"
    - "on the automatic generation of medical imaging reports"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "learning to summarize radiology findings"
    - "unsupervised multimodal representation learning across medical images and reports"

Connected through hyperedge HE-3 (induced by source paper "joint entity extraction and assertion detection for clinical text"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "comprehend medical a named entity recognition and relationship extraction web service"
    - "dynamic transfer learning for named entity recognition"
    - "improving hospital mortality prediction with medical named entities and multimodal learning"
    - "morphological priors for probabilistic neural word embeddings"
    - "enriching word vectors with subword information"
    - "multi task cross lingual sequence tagging from scratch"
    - "multi task domain adaptation for sequence tagging"

Connected through hyperedge HE-4 (induced by source paper "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"):
  with members:
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "data distillation towards omni supervised learning"

Connected through hyperedge HE-5 (induced by source paper "cross modal data programming enables rapid medical machine learning"):
  with members:
    - "attention based deep multiple instance learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "snorkel rapid training data creation with weak supervision"
    - "learning the structure of generative models without labeled data"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "learning to estimate label uncertainty for automatic radiology report parsing"):
  with members:
    - "a strong baseline for domain adaptation and generalization in medical imaging"
    - "caveats in generating medical imaging labels from radiology reports"
    - "learning to diagnose from scratch by exploiting dependencies among labels"
    - "efficient and accurate abnormality mining from radiology reports with customized false positive reduction"
    - "weakly supervised medical diagnosis and localization from multiple resolutions"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "classifying the reported ability in clinical mobility descriptions"):
  with members:
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "lessons from natural language inference in the clinical domain"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "enriching word vectors with subword information"
    - "character level convolutional networks for text classification"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-8 (induced by source paper "mimic cxr jpg a large publicly available database of labeled chest radiographs"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

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.CL(Computation and Language)