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: "modeling polypharmacy side effects with graph convolutional networks"
  Abstract: Motivation: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.#R##N##R##N#Results: Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug-drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

Connected through hyperedge HE-1 (induced by source paper "differentiable graph module dgm graph convolutional networks"):
  with members:
    - "brain signal classification via learning connectivity structure"
    - "graph neural networks with convolutional arma filters"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "link prediction based on graph neural networks"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "adaptive graph convolutional neural networks"
    - "dual primal graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "uwb gcn accelerating graph convolutional networks through runtime workload rebalancing"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "a comprehensive survey on graph neural networks"
    - "packing sparse convolutional neural networks for efficient systolic array implementations column combining under joint optimization"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "large scale learnable graph convolutional networks"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "simgnn a neural network approach to fast graph similarity computation"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-4 (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"
    - "gamenet graph augmented memory networks for recommending medication combination"
    - "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-5 (induced by source paper "automating botnet detection with graph neural networks"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "pytorch an imperative style high performance deep learning library"
    - "graph neural networks a review of methods and applications"
    - "machine learning ddos detection for consumer internet of things devices"
    - "neural message passing for quantum chemistry"
    - "learning to represent programs with graphs"
    - "semi supervised classification with graph convolutional networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "link prediction based on graph neural networks"
    - "link prediction in dynamic graphs for recommendation"
    - "multi task graph autoencoders"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "gemsec graph embedding with self clustering"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-7 (induced by source paper "a gentle introduction to deep learning for graphs"):
  with members:
    - "deep tree transductions a short survey"
    - "gmnn graph markov neural networks"
    - "a model to search for synthesizable molecules"
    - "graph u nets"
    - "a fair comparison of graph neural networks for graph classification"
    - "self attention graph pooling"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "imagegcn multi relational image graph convolutional networks for disease identification with chest x rays"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "graph convolutional neural networks for web scale recommender systems"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "attention guided curriculum learning for weakly supervised classification and localization of thoracic diseases on chest radiographs"
    - "dynamic routing on deep neural network for thoracic disease classification and sensitive area localization"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graph convolutional networks for text classification"

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)