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: "gamenet graph augmented memory networks for recommending medication combination"
  Abstract: Recent progress in deep learning is revolutionizing the healthcare domain including providing solutions to medication recommendations, especially recommending medication combination for patients with complex health conditions. Existing approaches either do not customize based on patient health history, or ignore existing knowledge on drug-drug interactions (DDI) that might lead to adverse outcomes. To fill this gap, we propose the Graph Augmented Memory Networks (GAMENet), which integrates the drug-drug interactions knowledge graph by a memory module implemented as a graph convolutional networks, and models longitudinal patient records as the query. It is trained end-to-end to provide safe and personalized recommendation of medication combination. We demonstrate the effectiveness and safety of GAMENet by comparing with several state-of-the-art methods on real EHR data. GAMENet outperformed all baselines in all effectiveness measures, and also achieved 3.60% DDI rate reduction from existing EHR data.

Connected through hyperedge HE-1 (induced by source paper "tri graph information propagation for polypharmacy side effect prediction"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "knowledge graph completion to predict polypharmacy side effects"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-2 (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-3 (induced by source paper "pre training of graph augmented transformers for medication recommendation"):
  with members:
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "unsupervised pretraining for sequence to sequence learning"
    - "split brain autoencoders unsupervised learning by cross channel prediction"

Connected through hyperedge HE-4 (induced by source paper "beyond similarity relation embedding with dual attentions for item based recommendation"):
  with members:
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "collaborative memory network for recommendation systems"
    - "neural collaborative filtering"
    - "deep cross network for ad click predictions"
    - "related pins at pinterest the evolution of a real world recommender system"
    - "key value memory networks for directly reading documents"
    - "end to end memory networks"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "doctor2vec dynamic doctor representation learning for clinical trial recruitment"):
  with members:
    - "pre training of graph augmented transformers for medication recommendation"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "attention is all you need"
    - "key value memory networks for directly reading documents"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "end to end memory networks"

Connected through hyperedge HE-6 (induced by source paper "medgcn graph convolutional networks for multiple medical tasks"):
  with members:
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "representation learning on graphs methods and applications"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"

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.AI(Artificial Intelligence)