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: "modelling radiological language with bidirectional long short term memory networks"
  Abstract: Motivated by the need to automate medical information extraction from free-text radiological reports, we present a bi-directional long short-term memory (BiLSTM) neural network architecture for modelling radiological language. The model has been used to address two NLP tasks: medical named-entity recognition (NER) and negation detection. We investigate whether learning several types of word embeddings improves BiLSTM's performance on those tasks. Using a large dataset of chest x-ray reports, we compare the proposed model to a baseline dictionary-based NER system and a negation detection system that leverages the hand-crafted rules of the NegEx algorithm and the grammatical relations obtained from the Stanford Dependency Parser. Compared to these more traditional rule-based systems, we argue that BiLSTM offers a strong alternative for both our tasks.

Connected through hyperedge HE-1 (induced by source paper "understanding spatial language in radiology representation framework annotation and spatial relation extraction from chest x ray reports using deep learning"):
  with members:
    - "chexpert a large chest radiograph dataset with uncertainty labels and expert comparison"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "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"
    - "learning to read chest x rays recurrent neural cascade model for automated image annotation"
    - "neural architectures for named entity recognition"
    - "end to end relation extraction using lstms on sequences and tree structures"

Connected through hyperedge HE-2 (induced by source paper "named entity recognition for electronic health records a comparison of rule based and machine learning approaches"):
  with members:
    - "bidirectional lstm crf models for sequence tagging"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "learning to summarize radiology findings"):
  with members:
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "get to the point summarization with pointer generator networks"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "pointer sentinel mixture models"
    - "a neural attention model for abstractive sentence summarization"
    - "effective approaches to attention based neural machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"):
  with members:
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "unsupervised joint mining of deep features and image labels for large scale radiology image categorization and scene recognition"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"

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)