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: "drug similarity integration through attentive multi view graph auto encoders"
  Abstract: Drug similarity has been studied to support downstream clinical tasks such as inferring novel properties of drugs (e.g. side effects, indications, interactions) from known properties. The growing availability of new types of drug features brings the opportunity of learning a more comprehensive and accurate drug similarity that represents the full spectrum of underlying drug relations. However, it is challenging to integrate these heterogeneous, noisy, nonlinear-related information to learn accurate similarity measures especially when labels are scarce. Moreover, there is a trade-off between accuracy and interpretability. In this paper, we propose to learn accurate and interpretable similarity measures from multiple types of drug features. In particular, we model the integration using multi-view graph auto-encoders, and add attentive mechanism to determine the weights for each view with respect to corresponding tasks and features for better interpretability. Our model has flexible design for both semi-supervised and unsupervised settings. Experimental results demonstrated significant predictive accuracy improvement. Case studies also showed better model capacity (e.g. embed node features) and interpretability.

Connected through hyperedge HE-1 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study"):
  with members:
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "adversarial attack on community detection by hiding individuals"
    - "transferring robustness for graph neural network against poisoning attacks"
    - "graph universal adversarial attacks a few bad actors ruin graph learning models"
    - "adversarial training methods for network embedding"
    - "adversarial examples on graph data deep insights into attack and defense"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "the general black box attack method for graph neural networks"

Connected through hyperedge HE-2 (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"
    - "hierarchical graph representation learning with differentiable pooling"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "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-3 (induced by source paper "tri graph information propagation for polypharmacy side effect prediction"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "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-4 (induced by source paper "graph embedding on biomedical networks methods applications and evaluations"):
  with members:
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "graph embedding techniques applications and performance a survey"
    - "universal language model fine tuning for text classification"
    - "network representation learning a survey"
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "representation learning on graphs methods and applications"
    - "graphgan graph representation learning with generative adversarial nets"

Connected through hyperedge HE-5 (induced by source paper "unsupervised inductive graph level representation learning via graph graph proximity"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-6 (induced by source paper "genn predicting correlated drug drug interactions with graph energy neural networks"):
  with members:
    - "gmnn graph markov neural networks"
    - "fast graph representation learning with pytorch geometric"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "learning approximate inference networks for structured prediction"
    - "neural message passing for quantum chemistry"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "structured prediction energy networks"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "med2meta learning representations of medical concepts with meta embeddings"):
  with members:
    - "mixed pooling multi view attention autoencoder for representation learning in healthcare"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "think globally embed locally locally linear meta embedding of words"
    - "medical concept representation learning from electronic health records and its application on heart failure prediction"
    - "gram graph based attention model for healthcare representation learning"
    - "learning word meta embeddings by using ensembles of embedding sets"
    - "a primer on neural network models for natural language processing"

Connected through hyperedge HE-8 (induced by source paper "inceptiongcn receptive field aware graph convolutional network for disease prediction"):
  with members:
    - "representation learning on graphs with jumping knowledge networks"
    - "multi layered parallel graph convolutional network ml pgcn for disease prediction"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "densely connected convolutional networks"
    - "semi supervised classification with graph convolutional networks"
    - "going deeper with convolutions"
    - "coast a convex optimization approach to stress based embedding"

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)