human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "augmented transformer achieves 97 and 85 for top5 prediction of direct and classical retro synthesis"):
  Abstract: We investigated the effect of different augmentation scenarios on predicting (retro)synthesis of chemical compounds using SMILES representation. We showed that augmentation of not only input sequences but also, importantly, of the target data eliminated the effect of data memorization by neural networks and improved their generalization performance for prediction of new sequences. The Top-5 accuracy was 85.4% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for USPTO-50k test dataset and was achieved by a combination of SMILES augmentation and beam search. The same approach also outperformed best published results for prediction of direct reactions from the USPTO-MIT test set. Our model achieved 90.4% Top-1 and 96.5% Top-5 accuracy for its most challenging mixed set and 97% Top-5 accuracy for the USPTO-MIT separated set. The appearance frequency of the most abundantly generated SMILES was well correlated with the prediction outcome and can be used as a measure of the quality of reaction prediction.

Includes members:
  Member "retrosynthesis prediction with conditional graph logic network":
    also connected through other hyperedges:
      (none)
  Member "learning to make generalizable and diverse predictions for retrosynthesis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "predicting retrosynthetic pathways using a combined linguistic model and hyper graph exploration strategy")
  Member "planning chemical syntheses with deep neural networks and symbolic ai":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "retrosynthesis prediction with conditional graph logic network")
  Member "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep generative model for sparse graphs using text based learning with augmentation in generative examination networks")
  Member "graph transformation policy network for chemical reaction prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph representation learning for audio music genre classification")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "retrosynthesis prediction with conditional graph logic network")
  Member "smiles enumeration as data augmentation for neural network modeling of molecules":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative models for automatic chemical design")

Question: Please tell me which class the source paper 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)