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: "pre training graph neural networks"
  Abstract: Many applications of machine learning in science and medicine, including molecular property and protein function prediction, can be cast as problems of predicting some properties of graphs, where having good graph representations is critical. However, two key challenges in these domains are (1) extreme scarcity of labeled data due to expensive lab experiments, and (2) needing to extrapolate to test graphs that are structurally different from those seen during training. In this paper, we explore pre-training to address both of these challenges. In particular, working with Graph Neural Networks (GNNs) for representation learning of graphs, we wish to obtain node representations that (1) capture similarity of nodes' network neighborhood structure, (2) can be composed to give accurate graph-level representations, and (3) capture domain-knowledge. To achieve these goals, we propose a series of methods to pre-train GNNs at both the node-level and the graph-level, using both unlabeled data and labeled data from related auxiliary supervised tasks. We perform extensive evaluation on two applications, molecular property and protein function prediction. We observe that performing only graph-level supervised pre-training often leads to marginal performance gain or even can worsen the performance compared to non-pre-trained models. On the other hand, effectively combining both node- and graph-level pre-training techniques significantly improves generalization to out-of-distribution graphs, consistently outperforming non-pre-trained GNNs across 8 datasets in molecular property prediction (resp. 40 tasks in protein function prediction), with the average ROC-AUC improvement of 7.2% (resp. 11.7%).

Connected through hyperedge HE-1 (induced by source paper "a comprehensive study on the prediction reliability of graph neural networks for virtual screening"):
  with members:
    - "bayesian deep learning and a probabilistic perspective of generalization"
    - "when does label smoothing help"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "data efficient image recognition with contrastive predictive coding"
    - "self attention graph pooling"
    - "dropconnect is effective in modeling uncertainty of bayesian deep networks"
    - "bayesian semi supervised learning for uncertainty calibrated prediction of molecular properties and active learning"
    - "using pre training can improve model robustness and uncertainty"

Connected through hyperedge HE-2 (induced by source paper "improving molecular design by stochastic iterative target augmentation"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "conditional molecular design with deep generative models"
    - "unsupervised data augmentation for consistency training"
    - "mixmatch a holistic approach to semi supervised learning"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep reinforcement learning for de novo drug design"
    - "design by adaptive sampling"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-3 (induced by source paper "meta learning initializations for low resource drug discovery"):
  with members:
    - "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"
    - "rapid learning or feature reuse towards understanding the effectiveness of maml"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "potentialnet for molecular property prediction"
    - "how powerful are graph neural networks"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-4 (induced by source paper "learning graph embedding with limited labeled data an efficient sampling approach"):
  with members:
    - "graph transformer networks"
    - "simplifying graph convolutional networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "topology adaptive graph convolutional networks"
    - "sampling online social networks by random walk with indirect jumps"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-5 (induced by source paper "infograph unsupervised and semi supervised graph level representation learning via mutual information maximization"):
  with members:
    - "utilizing edge features in graph neural networks via variational information maximization"
    - "fast graph representation learning with pytorch geometric"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-6 (induced by source paper "learning to make generalizable and diverse predictions for retrosynthesis"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "junction tree variational autoencoder for molecular graph generation"
    - "sequence to sequence mixture model for diverse machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

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)