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 write notes in electronic health records"
  Abstract: Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete.

Connected through hyperedge HE-1 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring"):
  with members:
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "building a conversational agent overnight with dialogue self play"
    - "know what you don t know unanswerable questions for squad"
    - "coqa a conversational question answering challenge"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "stochastic answer networks for squad 2 0"
    - "quac question answering in context"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "is artificial data useful for biomedical natural language processing algorithms"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "investigating backtranslation in neural machine translation"
    - "natural language generation for electronic health records"
    - "on the automatic generation of medical imaging reports"
    - "learning neural templates for text generation"
    - "hierarchical neural story generation"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "unsupervised multimodal representation learning across medical images and reports"):
  with members:
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "large scale automated reading of frontal and lateral chest x rays using dual convolutional neural networks"
    - "producing radiologist quality reports for interpretable artificial intelligence"
    - "universal sentence encoder"
    - "large scale gan training for high fidelity natural image synthesis"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "a universal music translation network"
    - "self normalizing neural networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised pseudo labeling for extractive summarization on electronic health records"):
  with members:
    - "reinforced extractive summarization with question focused rewards"
    - "neural summarization by extracting sentences and words"
    - "attsum joint learning of focusing and summarization with neural attention"
    - "summarunner a recurrent neural network based sequence model for extractive summarization of documents"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)