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: "gram graph based attention model for healthcare representation learning"
  Abstract: Deep learning methods exhibit promising performance for predictive modeling in healthcare, but two important challenges remain: -Data insufficiency:Often in healthcare predictive modeling, the sample size is insufficient for deep learning methods to achieve satisfactory results. -Interpretation:The representations learned by deep learning methods should align with medical knowledge. To address these challenges, we propose a GRaph-based Attention Model, GRAM that supplements electronic health records (EHR) with hierarchical information inherent to medical ontologies. Based on the data volume and the ontology structure, GRAM represents a medical concept as a combination of its ancestors in the ontology via an attention mechanism. We compared predictive performance (i.e. accuracy, data needs, interpretability) of GRAM to various methods including the recurrent neural network (RNN) in two sequential diagnoses prediction tasks and one heart failure prediction task. Compared to the basic RNN, GRAM achieved 10% higher accuracy for predicting diseases rarely observed in the training data and 3% improved area under the ROC curve for predicting heart failure using an order of magnitude less training data. Additionally, unlike other methods, the medical concept representations learned by GRAM are well aligned with the medical ontology. Finally, GRAM exhibits intuitive attention behaviors by adaptively generalizing to higher level concepts when facing data insufficiency at the lower level concepts.

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"
    - "patient representation learning and interpretable evaluation using clinical notes"
    - "semantic structure and interpretability of word embeddings"
    - "improving medical short text classification with semantic expansion using word cluster embedding"

Connected through hyperedge HE-2 (induced by source paper "unsupervised hierarchical graph representation learning by mutual information maximization"):
  with members:
    - "fobe and hobe first and high order bipartite embeddings"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "large scale learnable graph convolutional networks"
    - "janossy pooling learning deep permutation invariant functions for variable size inputs"
    - "harp hierarchical representation learning for networks"
    - "neural message passing for quantum chemistry"
    - "graph2vec learning distributed representations of graphs"
    - "predicting organic reaction outcomes with weisfeiler lehman network"

Connected through hyperedge HE-3 (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-4 (induced by source paper "language models are an effective patient representation learning technique for electronic health record data"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "deep transfer learning for physiological signals"):
  with members:
    - "explainable ai for trees from local explanations to global understanding"
    - "transfer learning for clinical time series analysis using recurrent neural networks"
    - "deep transfer learning for eeg based brain computer interface"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "a unified approach to interpreting model predictions"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "frustratingly easy domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments"):
  with members:
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "scalable and accurate deep learning for electronic health records"
    - "fogbus a blockchain based lightweight framework for edge and fog computing"
    - "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"
    - "foglearn leveraging fog based machine learning for smart system big data analytics"
    - "aneka a software platform for net based cloud computing"

Connected through hyperedge HE-7 (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-8 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph 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)