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: "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
  Abstract: There is an intuitive analogy of an organic chemist's understanding of a compound and a language speaker's understanding of a word. Consequently, it is possible to introduce the basic concepts and analyze potential impacts of linguistic analysis to the world of organic chemistry. In this work, we cast the reaction prediction task as a translation problem by introducing a template-free sequence-to-sequence model, trained end-to-end and fully data-driven. We propose a novel way of tokenization, which is arbitrarily extensible with reaction information. With this approach, we demonstrate results superior to the state-of-the-art solution by a significant margin on the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1% without relying on auxiliary knowledge such as reaction templates. Also, 66.4% accuracy is reached on a larger and noisier dataset.

Connected through hyperedge HE-1 (induced by source paper "retrosynthesis prediction with conditional graph logic network"):
  with members:
    - "learning retrosynthetic planning through self play"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "deriving neural architectures from sequence and graph kernels"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "gated graph sequence neural networks"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-2 (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"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"

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 "a model to search for synthesizable molecules"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "molecular sets moses a benchmarking platform for molecular generation models"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "improving chemical named entity recognition in patents with contextualized word embeddings"):
  with members:
    - "using similarity measures to select pretraining data for ner"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "comparing cnn and lstm character level embeddings in bilstm crf models for chemical and disease named entity recognition"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"

Connected through hyperedge HE-6 (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"
    - "moleculenet a benchmark for molecular machine learning"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"

Connected through hyperedge HE-7 (induced by source paper "chembo bayesian optimization of small organic molecules with synthesizable recommendations"):
  with members:
    - "a model to search for synthesizable molecules"
    - "conditional molecular design with deep generative models"
    - "optimization of molecules via deep reinforcement learning"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "neural architecture search with bayesian optimisation and optimal transport"
    - "chemi net a graph convolutional network for accurate drug property prediction"
    - "deriving neural architectures from sequence and graph kernels"

Connected through hyperedge HE-8 (induced by source paper "re balancing variational autoencoder loss for molecule sequence generation"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "improved variational autoencoders for text modeling using dilated convolutions"

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)