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: "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
  Abstract: Predicting the future health information of patients from the historical Electronic Health Records (EHR) is a core research task in the development of personalized healthcare. Patient EHR data consist of sequences of visits over time, where each visit contains multiple medical codes, including diagnosis, medication, and procedure codes. The most important challenges for this task are to model the temporality and high dimensionality of sequential EHR data and to interpret the prediction results. Existing work solves this problem by employing recurrent neural networks (RNNs) to model EHR data and utilizing simple attention mechanism to interpret the results. However, RNN-based approaches suffer from the problem that the performance of RNNs drops when the length of sequences is large, and the relationships between subsequent visits are ignored by current RNN-based approaches. To address these issues, we propose Dipole, an end-to-end, simple and robust model for predicting patients' future health information. Dipole employs bidirectional recurrent neural networks to remember all the information of both the past visits and the future visits, and it introduces three attention mechanisms to measure the relationships of different visits for the prediction. With the attention mechanisms, Dipole can interpret the prediction results effectively. Dipole also allows us to interpret the learned medical code representations which are confirmed positively by medical experts. Experimental results on two real world EHR datasets show that the proposed Dipole can significantly improve the prediction accuracy compared with the state-of-the-art diagnosis prediction approaches and provide clinically meaningful interpretation.

Connected through hyperedge HE-1 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data"):
  with members:
    - "scalpel3 a scalable open source library for healthcare claims databases"
    - "behrt transformer for electronic health records"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "clinical concept embeddings learned from massive sources of multimodal medical data"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "graph driven generative models for heterogeneous multi task learning"):
  with members:
    - "medgcn graph convolutional networks for multiple medical tasks"
    - "improving textual network learning with variational homophilic embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "graph convolutional networks for text classification"
    - "joint embedding of words and labels for text classification"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"

Connected through hyperedge HE-3 (induced by source paper "graph convolutional transformer learning the graphical structure of electronic health records"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "behrt transformer for electronic health records"
    - "inpatient2vec medical representation learning for inpatients"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"):
  with members:
    - "exploring dimensionality reductions with forward and backward projections"
    - "brain tumor segmentation with deep neural networks"
    - "understanding hidden memories of recurrent neural networks"
    - "direct manipulation visualization of deep networks"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "towards better analysis of deep convolutional neural networks"
    - "the mythos of model interpretability"

Connected through hyperedge HE-5 (induced by source paper "learning the graphical structure of electronic health records with graph convolutional transformer"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "resset a recurrent model for sequence of sets with applications to electronic medical records"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "scalable and accurate deep learning for electronic health records"
    - "clinical intervention prediction and understanding using deep networks"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "temporal clustering invariance in irregular healthcare time series"):
  with members:
    - "on the convergence of adam and beyond"
    - "interpolation prediction networks for irregularly sampled time series"
    - "learning to exploit invariances in clinical time series data using sequence transformer networks"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "modeling missing data in clinical time series with rnns"
    - "group equivariant convolutional networks"
    - "recurrent neural networks for multivariate time series with missing values"
    - "beyond short snippets deep networks for video classification"

Connected through hyperedge HE-7 (induced by source paper "hierarchical target attentive diagnosis prediction in heterogeneous information networks"):
  with members:
    - "hahe hierarchical attentive heterogeneous information network embedding"
    - "heteromed heterogeneous information network for medical diagnosis"
    - "attention is all you need"
    - "benchmark of deep learning models on large healthcare mimic datasets"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "gram graph based attention model for healthcare representation learning"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"

Connected through hyperedge HE-8 (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"
    - "prediction constrained topic models for antidepressant recommendation"

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.LG(Machine Learning)