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: "learning to summarize radiology findings"
  Abstract: The Impression section of a radiology report summarizes crucial radiology findings in natural language and plays a central role in communicating these findings to physicians. However, the process of generating impressions by summarizing findings is time-consuming for radiologists and prone to errors. We propose to automate the generation of radiology impressions with neural sequence-to-sequence learning. We further propose a customized neural model for this task which learns to encode the study background information and use this information to guide the decoding process. On a large dataset of radiology reports collected from actual hospital studies, our model outperforms existing non-neural and neural baselines under the ROUGE metrics. In a blind experiment, a board-certified radiologist indicated that 67% of sampled system summaries are at least as good as the corresponding human-written summaries, suggesting significant clinical validity. To our knowledge our work represents the first attempt in this direction.

Connected through hyperedge HE-1 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "a neural compositional paradigm for image captioning"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "producing radiologist quality reports for interpretable artificial intelligence"

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"
    - "unsupervised multimodal representation learning across medical images and reports"
    - "bimodal network architectures for automatic generation of image annotation from text"

Connected through hyperedge HE-3 (induced by source paper "optimizing the factual correctness of a summary a study of summarizing radiology reports"):
  with members:
    - "evaluating the factual consistency of abstractive text summarization"
    - "assessing the factual accuracy of generated text"
    - "ontology aware clinical abstractive summarization"
    - "generating wikipedia by summarizing long sequences"
    - "on the automatic generation of medical imaging reports"
    - "banditsum extractive summarization as a contextual bandit"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "fast abstractive summarization with reinforce selected sentence rewriting"

Connected through hyperedge HE-4 (induced by source paper "communication based evaluation for natural language generation"):
  with members:
    - "unifying human and statistical evaluation for natural language generation"
    - "pragmatically informative text generation"
    - "generating bilingual pragmatic color references"
    - "translating neuralese"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "sentence level fluency evaluation references help but can be spared"
    - "context aware captions from context agnostic supervision"

Connected through hyperedge HE-5 (induced by source paper "towards understanding of medical randomized controlled trials by conclusion generation"):
  with members:
    - "transfer learning in biomedical natural language processing an evaluation of bert and elmo on ten benchmarking datasets"
    - "doubletransfer at mediqa 2019 multi source transfer learning for natural language understanding in the medical domain"
    - "a unified model for extractive and abstractive summarization using inconsistency loss"
    - "on the automatic generation of medical imaging reports"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a discourse aware attention model for abstractive summarization of long documents"

Connected through hyperedge HE-6 (induced by source paper "ontology aware clinical abstractive summarization"):
  with members:
    - "bottom up abstractive summarization"
    - "a discourse aware attention model for abstractive summarization of long documents"
    - "get to the point summarization with pointer generator networks"
    - "challenges in data to document generation"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "revisiting summarization evaluation for scientific articles"
    - "a neural attention model for abstractive sentence summarization"

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)