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: "retrosynthetic reaction prediction using neural sequence to sequence models"
  Abstract: We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder-decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step towards solving the challenging problem of computational retrosynthetic analysis.

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 "world programs for model based learning and planning in compositional state and action spaces"):
  with members:
    - "a model to search for synthesizable molecules"
    - "challenges of real world reinforcement learning"
    - "structured agents for physical construction"
    - "learning to represent edits"
    - "generative code modeling with graphs"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive bias for physical construction in humans and machines"
    - "leveraging grammar and reinforcement learning for neural program synthesis"

Connected through hyperedge HE-3 (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-4 (induced by source paper "enhanced deep learning prediction of molecular properties via augmentation of bond topology"):
  with members:
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "how powerful are graph neural networks"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "modelling chemical reasoning to predict and invent reactions"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-5 (induced by source paper "learning to make generalizable and diverse predictions for retrosynthesis"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "pre training graph neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "junction tree variational autoencoder for molecular graph generation"
    - "sequence to sequence mixture model for diverse machine translation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "graph convolutional policy network for goal directed molecular graph generation"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "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"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"
    - "molecular de novo design through deep reinforcement learning"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "discovering molecular functional groups using graph convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep learning for molecular design a review of the state of the art"
    - "constrained graph variational autoencoders for molecule design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "smoothgrad removing noise by adding noise"
    - "neural message passing for quantum chemistry"
    - "moleculenet a benchmark for molecular machine learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "planning chemical syntheses with deep neural networks and symbolic ai"):
  with members:
    - "end to end differentiable proving"
    - "modelling chemical reasoning to predict and invent reactions"
    - "monte carlo tableau proof search"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "building machines that learn and think like people"
    - "theano a python framework for fast computation of mathematical expressions"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "training very deep networks"

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)