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: "low data drug discovery with one shot learning"
  Abstract: Recent advances in machine learning have made significant contributions to drug discovery. Deep neural networks in particular have been demonstrated to provide significant boosts in predictive power when inferring the properties and activities of small-molecule compounds. However, the applicability of these techniques has been limited by the requirement for large amounts of training data. In this work, we demonstrate how one-shot learning can be used to significantly lower the amounts of data required to make meaningful predictions in drug discovery applications. We introduce a new architecture, the residual LSTM embedding, that, when combined with graph convolutional neural networks, significantly improves the ability to learn meaningful distance metrics over small-molecules. We open source all models introduced in this work as part of DeepChem, an open-source framework for deep-learning in drug discovery.

Connected through hyperedge HE-1 (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"
    - "pre training graph neural networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a structured prediction approach for conditional meta learning"):
  with members:
    - "meta learning with implicit gradients"
    - "on first order meta learning algorithms"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-3 (induced by source paper "unraveling meta learning understanding feature representations for few shot tasks"):
  with members:
    - "a baseline for few shot image classification"
    - "fast and generalized adaptation for few shot learning"
    - "do better imagenet models transfer better"
    - "reptile a scalable metalearning algorithm"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta learning with differentiable closed form solvers"
    - "prototypical networks for few shot learning"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "fast haar transforms for graph neural networks"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "haarpooling graph pooling with compressive haar basis"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-5 (induced by source paper "relational pooling for graph representations"):
  with members:
    - "on the universality of invariant networks"
    - "pitfalls of graph neural network evaluation"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep sets"
    - "relational inductive biases deep learning and graph networks"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-6 (induced by source paper "haar graph pooling"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "fast haar transforms for graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "graph convolutional networks with eigenpooling"
    - "self attention graph pooling"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "kekulescope prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images"):
  with members:
    - "deep confidence a computationally efficient framework for calculating reliable prediction errors for deep neural networks"
    - "hydranet data augmentation for regression neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "improving deep learning using generic data augmentation"
    - "molecular de novo design through deep reinforcement learning"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-8 (induced by source paper "smiles transformer pre trained molecular fingerprint for low data drug discovery"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "deep contextualized word representations"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "junction tree variational autoencoder for molecular graph generation"

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)