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: "neural document embeddings for intensive care patient mortality prediction"
  Abstract: We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes. Proposing a convolutional document embedding approach, our empirical investigation using the MIMIC-III intensive care database shows significant performance gains compared to previously employed methods such as latent topic distributions or generic doc2vec embeddings. These improvements are especially pronounced for the difficult problem of post-discharge mortality prediction.

Connected through hyperedge HE-1 (induced by source paper "structure tree lstm structure aware attentional document encoders"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-2 (induced by source paper "interpretable structure aware document encoders with hierarchical attention"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "tree to sequence attentional neural machine translation"
    - "long short term memory networks for machine reading"
    - "enhanced lstm for natural language inference"
    - "enriching word vectors with subword information"
    - "learning distributed representations of sentences from unlabelled data"

Connected through hyperedge HE-3 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring"):
  with members:
    - "learning to write notes in electronic health records"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning conceptual contexual embeddings for medical text"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "ernie enhanced language representation with informative entities"
    - "beyond word embeddings learning entity and concept representations from large scale knowledge bases"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "deep contextualized word representations"
    - "lessons from natural language inference in the clinical domain"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "biosentvec creating sentence embeddings for biomedical texts"

Connected through hyperedge HE-5 (induced by source paper "machine learning and visualization in clinical decision support current state and future directions"):
  with members:
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "spartan scalable parafac2 for large sparse data"
    - "continuous state space models for optimal sepsis treatment a deep reinforcement learning approach"
    - "representation and reinforcement learning for personalized glycemic control in septic patients"
    - "improving palliative care with deep learning"
    - "the use of autoencoders for discovering patient phenotypes"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards unstructured mortality prediction with free text clinical notes"):
  with members:
    - "iseeu visually interpretable deep learning for mortality prediction inside the icu"
    - "on the convergence of adam and beyond"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "machine learning for structured clinical data"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "attention is all you need"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-7 (induced by source paper "hierarchical semantic correspondence learning for post discharge patient mortality prediction"):
  with members:
    - "joint slot filling and intent detection via capsule neural networks"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "deep contextualized word representations"
    - "deep relevance ranking using enhanced document query interactions"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "clinical concept extraction with contextual word embedding"
    - "deconvolutional paragraph representation learning"
    - "scnet learning semantic correspondence"

Connected through hyperedge HE-8 (induced by source paper "multimodal ensemble approach to incorporate various types of clinical notes for predicting readmission"):
  with members:
    - "classification of radiology reports using neural attention models"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"

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)