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: "modelling chemical reasoning to predict and invent reactions"
  Abstract: The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.

Connected through hyperedge HE-1 (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-2 (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-3 (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"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-4 (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"
    - "improved techniques for training gans"
    - "linking the neural machine translation and the prediction of organic chemistry reactions"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning for de novo drug design"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "multi step reinforcement learning a unifying algorithm"
    - "molecular de novo design through deep reinforcement learning"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "learning operations on a stack with neural turing machines"
    - "low data drug discovery with one shot learning"
    - "learning to transduce with unbounded memory"
    - "visualizing and understanding recurrent networks"

Connected through hyperedge HE-6 (induced by source paper "planning chemical syntheses with deep neural networks and symbolic ai"):
  with members:
    - "end to end differentiable proving"
    - "monte carlo tableau proof search"
    - "retrosynthetic reaction prediction using neural sequence to sequence models"
    - "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"

Connected through hyperedge HE-7 (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"
    - "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"
    - "linking the neural machine translation and the prediction of organic chemistry reactions"
    - "low data drug discovery with one shot learning"

Connected through hyperedge HE-8 (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"
    - "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"
    - "linking the neural machine translation and the prediction of organic chemistry reactions"
    - "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.AI(Artificial Intelligence)