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: "patient representation learning and interpretable evaluation using clinical notes"
  Abstract: Abstract   We have three contributions in this work: 1. We explore the utility of a stacked denoising autoencoder and a paragraph vector model to learn task-independent dense patient representations directly from clinical notes. To analyze if these representations are transferable across tasks, we evaluate them in multiple supervised setups to predict patient mortality, primary diagnostic and procedural category, and gender. We compare their performance with sparse representations obtained from a bag-of-words model. We observe that the learned generalized representations significantly outperform the sparse representations when we have few positive instances to learn from, and there is an absence of strong lexical features. 2. We compare the model performance of the feature set constructed from a bag of words to that obtained from medical concepts. In the latter case, concepts represent problems, treatments, and tests. We find that concept identification does not improve the classification performance. 3. We propose novel techniques to facilitate model interpretability. To understand and interpret the representations, we explore the best encoded features within the patient representations obtained from the autoencoder model. Further, we calculate feature sensitivity across two networks to identify the most significant input features for different classification tasks when we use these pretrained representations as the supervised input. We successfully extract the most influential features for the pipeline using this technique.

Connected through hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing"):
  with members:
    - "classifying medical relations in clinical text via convolutional neural networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "explainable prediction of medical codes from clinical text"
    - "embedding transfer for low resource medical named entity recognition a case study on patient mobility"
    - "medical concept embedding with time aware attention"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"
    - "clinical text classification with rule based features and knowledge guided convolutional neural networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "neural document embeddings for intensive care patient mortality prediction"

Connected through hyperedge HE-3 (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"
    - "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"
    - "neural document embeddings for intensive care patient mortality prediction"

Connected through hyperedge HE-4 (induced by source paper "rule induction for global explanation of trained models"):
  with members:
    - "evaluating neural network explanation methods using hybrid documents and morphological agreement"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "clinical intervention prediction and understanding using deep networks"
    - "interpretable explorable approximations of black box models"
    - "learning important features through propagating activation differences"
    - "what is relevant in a text document an interpretable machine learning approach"
    - "improving palliative care with deep learning"
    - "interpreting neural networks to improve politeness comprehension"

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)