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: "linking the neural machine translation and the prediction of organic chemistry reactions"
  Abstract: Finding the main product of a chemical reaction is one of the important problems of organic chemistry. This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions. In order to translate 'reactants and reagents' to 'products', a gated recurrent unit based sequence-to-sequence model and a parser to generate input tokens for model from reaction SMILES strings were built. Training sets are composed of reactions from the patent databases, and reactions manually generated applying the elementary reactions in an organic chemistry textbook of Wade. The trained models were tested by examples and problems in the textbook. The prediction process does not need manual encoding of rules (e.g., SMARTS transformations) to predict products, hence it only needs sufficient training reaction sets to learn new types of reactions.

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"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"

Connected through hyperedge HE-2 (induced by source paper "predicting retrosynthetic pathways using a combined linguistic model and hyper graph exploration strategy"):
  with members:
    - "learning to make generalizable and diverse predictions for retrosynthesis"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "modelling chemical reasoning to predict and invent reactions"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "graph transformation policy network for chemical reaction prediction"):
  with members:
    - "relational deep reinforcement learning"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph memory networks for molecular activity prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "non local neural networks"
    - "attention is all you need"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "go for a walk and arrive at the answer reasoning over paths in knowledge bases using reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "retrosynthetic reaction prediction using neural sequence to sequence models"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "massive exploration of neural machine translation architectures"
    - "modelling chemical reasoning to predict and invent reactions"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "one shot learning with memory augmented neural networks"
    - "learning to smile s"
    - "matching networks for one shot learning"
    - "low data drug discovery with one shot learning"

Connected through hyperedge HE-5 (induced by source paper "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "modelling chemical reasoning to predict and invent reactions"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "predicting organic reaction outcomes with weisfeiler lehman network"
    - "tensorflow a system for large scale machine learning"
    - "effective approaches to attention based 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)