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: "improving hospital mortality prediction with medical named entities and multimodal learning"
  Abstract: Clinical text provides essential information to estimate the acuity of a patient during hospital stays in addition to structured clinical data. In this study, we explore how clinical text can complement a clinical predictive learning task. We leverage an internal medical natural language processing service to perform named entity extraction and negation detection on clinical notes and compose selected entities into a new text corpus to train document representations. We then propose a multimodal neural network to jointly train time series signals and unstructured clinical text representations to predict the in-hospital mortality risk for ICU patients. Our model outperforms the benchmark by 2% AUC.

Connected through hyperedge HE-1 (induced by source paper "assessment of amazon comprehend medical medication information extraction"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "dynamic transfer learning for named entity recognition"

Connected through hyperedge HE-2 (induced by source paper "latte latent type modeling for biomedical entity linking"):
  with members:
    - "medmentions a large biomedical corpus annotated with umls concepts"
    - "improving entity linking by modeling latent relations between mentions"
    - "deeptype multilingual entity linking by neural type system evolution"
    - "hierarchical losses and new resources for fine grained entity typing and linking"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "capturing semantic similarity for entity linking with convolutional neural networks"
    - "a deep relevance matching model for ad hoc retrieval"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-3 (induced by source paper "comprehend medical a named entity recognition and relationship extraction web service"):
  with members:
    - "relation extraction using explicit context conditioning"
    - "a walk based model on entity graphs for relation extraction"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "dynamic transfer learning for named entity recognition"
    - "a hybrid deep learning approach for medical relation extraction"
    - "global normalization of convolutional neural networks for joint entity and relation classification"
    - "joint extraction of entities and relations based on a novel tagging scheme"

Connected through hyperedge HE-4 (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"
    - "negbio a high performance tool for negation and uncertainty detection in radiology reports"
    - "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-5 (induced by source paper "relation extraction using explicit context conditioning"):
  with members:
    - "a walk based model on entity graphs for relation extraction"
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "self attention with relative position representations"
    - "a hybrid deep learning approach for medical relation extraction"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "a survey of deep learning methods for relation extraction"

Connected through hyperedge HE-6 (induced by source paper "using clinical notes with time series data for icu management"):
  with members:
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "explainable prediction of medical codes from clinical text"
    - "raim recurrent attentive and intensive model of multimodal patient monitoring data"
    - "learning tasks for multitask learning heterogenous patient populations in the icu"
    - "biosentvec creating sentence embeddings for biomedical texts"
    - "rationale augmented convolutional neural networks for text classification"
    - "explain images with multimodal recurrent neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-7 (induced by source paper "an analysis of attention over clinical notes for predictive tasks"):
  with members:
    - "attention is not explanation"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "understanding neural networks through representation erasure"
    - "learning to diagnose with lstm recurrent neural networks"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "dynamic transfer learning for named entity recognition"):
  with members:
    - "joint entity extraction and assertion detection for clinical text"
    - "simultaneously self attending to all mentions for full abstract biological relation extraction"
    - "the natural language decathlon multitask learning as question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "label aware double transfer learning for cross specialty medical named entity recognition"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "dynamic multi level multi task learning for sentence simplification"
    - "transfer learning for sequence tagging with hierarchical recurrent networks"

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)