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: "mime multilevel medical embedding of electronic health records for predictive healthcare"
  Abstract: Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems. External resources such as medical ontologies are used to bridge the data volume constraint, but this approach is often not directly applicable or useful because of inconsistencies with terminology. To solve the data insufficiency challenge, we leverage the inherent multilevel structure of EHR data and, in particular, the encoded relationships among medical codes. We propose Multilevel Medical Embedding (MiME) which learns the multilevel embedding of EHR data while jointly performing auxiliary prediction tasks that rely on this inherent EHR structure without the need for external labels. We conducted two prediction tasks, heart failure prediction and sequential disease prediction, where MiME outperformed baseline methods in diverse evaluation settings. In particular, MiME consistently outperformed all baselines when predicting heart failure on datasets of different volumes, especially demonstrating the greatest performance improvement (15% relative gain in PR-AUC over the best baseline) on the smallest dataset, demonstrating its ability to effectively model the multilevel structure of EHR data.

Connected through hyperedge HE-1 (induced by source paper "deepenroll patient trial matching with deep embedding and entailment prediction"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "publicly available clinical bert embeddings"
    - "learning eligibility in cancer clinical trials using deep neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "identity aware textual visual matching with latent co attention"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "baseline needs more love on simple word embedding based models and associated pooling mechanisms"
    - "leveraging clinical time series data for prediction a cautionary tale"
    - "universal language model fine tuning for text classification"
    - "scalable and accurate deep learning for electronic health records"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "improving palliative care with deep learning"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"

Connected through hyperedge HE-3 (induced by source paper "representation learning for electronic health records"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "multimodal multitask representation learning for pathology biobank metadata prediction"
    - "enhancing clinical concept extraction with contextual embeddings"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "clinical concept extraction for document level coding"
    - "graph convolutional transformer learning the graphical structure of electronic health records"
    - "unsupervised clinical language translation"
    - "publicly available clinical bert embeddings"

Connected through hyperedge HE-4 (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"
    - "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"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-5 (induced by source paper "temporal self attention network for medical concept embedding"):
  with members:
    - "prototype propagation networks ppn for weakly supervised few shot learning on category graph"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "medical concept embedding with time aware attention"
    - "integrative analysis of patient health records and neuroimages via memory based graph convolutional network"
    - "scalable and accurate deep learning for electronic health records"
    - "reinforced mnemonic reader for machine comprehension"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "taper time aware patient ehr representation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "clinicalbert modeling clinical notes and predicting hospital readmission"
    - "dynamic feature acquisition using denoising autoencoders"
    - "publicly available clinical bert embeddings"
    - "explainable prediction of medical codes from clinical text"
    - "medical concept embedding with time aware attention"
    - "deep ehr chronic disease prediction using medical notes"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "learning the graphical structure of electronic health records with graph convolutional transformer"):
  with members:
    - "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"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "graph neural network on electronic health records for predicting alzheimer s disease"):
  with members:
    - "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"
    - "attention is all you need"
    - "semi supervised classification with graph convolutional networks"
    - "gram graph based attention model for healthcare representation learning"
    - "effective approaches to attention based neural machine translation"
    - "deep convolutional networks on graph structured data"
    - "doctor ai predicting clinical events via recurrent neural 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.LG(Machine Learning)