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: "smiles enumeration as data augmentation for neural network modeling of molecules"
  Abstract: Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule. One molecule can however have multiple SMILES strings, which is a reason that canonical SMILES have been defined, which ensures a one to one correspondence between SMILES string and molecule. Here the fact that multiple SMILES represent the same molecule is explored as a technique for data augmentation of a molecular QSAR dataset modeled by a long short term memory (LSTM) cell based neural network. The augmented dataset was 130 times bigger than the original. The network trained with the augmented dataset shows better performance on a test set when compared to a model built with only one canonical SMILES string per molecule. The correlation coefficient R2 on the test set was improved from 0.56 to 0.66 when using SMILES enumeration, and the root mean square error (RMS) likewise fell from 0.62 to 0.55. The technique also works in the prediction phase. By taking the average per molecule of the predictions for the enumerated SMILES a further improvement to a correlation coefficient of 0.68 and a RMS of 0.52 was found.

Connected through hyperedge HE-1 (induced by source paper "augmented transformer achieves 97 and 85 for top5 prediction of direct and classical retro synthesis"):
  with members:
    - "retrosynthesis prediction with conditional graph logic network"
    - "learning to make generalizable and diverse predictions for retrosynthesis"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "graph transformation policy network for chemical reaction prediction"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "generative models for automatic chemical design"):
  with members:
    - "selfies a robust representation of semantically constrained graphs with an example application in chemistry"
    - "a model to search for synthesizable molecules"
    - "all smiles variational autoencoder"
    - "likelihood free inference and generation of molecular graphs"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "labeled graph generative adversarial networks"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "scaffold based molecular design using graph generative model"

Connected through hyperedge HE-3 (induced by source paper "deep learning for molecular design a review of the state of the art"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "conditional molecular design with deep generative models"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "mol cyclegan a generative model for molecular optimization"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "the gan landscape losses architectures regularization and normalization"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (induced by source paper "exploring bias in gan based data augmentation for small samples"):
  with members:
    - "gansfer learning combining labelled and unlabelled data for gan based data augmentation"
    - "imagining an engineer on gan based data augmentation perpetuating biases"
    - "t cgan conditional generative adversarial network for data augmentation in noisy time series with irregular sampling"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "code switching sentence generation by generative adversarial networks and its application to data augmentation"
    - "pixel level data augmentation for semantic image segmentation using generative adversarial networks"
    - "prostategan mitigating data bias via prostate diffusion imaging synthesis with generative adversarial networks"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"

Connected through hyperedge HE-7 (induced by source paper "deep generative model for sparse graphs using text based learning with augmentation in generative examination networks"):
  with members:
    - "gen highly efficient smiles explorer using autodidactic generative examination networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "syntax directed variational autoencoder for structured data"
    - "molecular de novo design through deep reinforcement learning"
    - "moleculenet a benchmark for molecular machine learning"

Connected through hyperedge HE-8 (induced by source paper "toward explainable anticancer compound sensitivity prediction via multimodal attention based convolutional encoders"):
  with members:
    - "paccmann prediction of anticancer compound sensitivity with multi modal attention based neural networks"
    - "deep reinforcement learning for de novo drug design"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "application of generative autoencoder in de novo molecular design"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "moleculenet a benchmark for molecular machine learning"

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)