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: "molecular generation with recurrent neural networks rnns"
  Abstract: The potential number of drug like small molecules is estimated to be between 10^23 and 10^60 while current databases of known compounds are orders of magnitude smaller with approximately 10^8 compounds. This discrepancy has led to an interest in generating virtual libraries using hand crafted chemical rules and fragment based methods to cover a larger area of chemical space and generate chemical libraries for use in in silico drug discovery endeavors. Here it is explored to what extent a recurrent neural network with long short term memory cells can figure out sensible chemical rules and generate synthesizable molecules by being trained on existing compounds encoded as SMILES. The networks can to a high extent generate novel, but chemically sensible molecules. The properties of the molecules are tuned by training on two different datasets consisting of fragment like molecules and drug like molecules. The produced molecules and the training databases have very similar distributions of molar weight, predicted logP, number of hydrogen bond acceptors and donors, number of rotatable bonds and topological polar surface area when compared to their respective training sets. The compounds are for the most cases synthesizable as assessed with SA score and Wiley ChemPlanner.

Connected through hyperedge HE-1 (induced by source paper "graph representation learning via graphical mutual information maximization"):
  with members:
    - "gmnn graph markov neural networks"
    - "a two step graph convolutional decoder for molecule generation"
    - "graph wavelet neural network"
    - "relation aware entity alignment for heterogeneous knowledge graphs"
    - "demo net degree specific graph neural networks for node and graph classification"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "regal representation learning based graph alignment"

Connected through hyperedge HE-2 (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-3 (induced by source paper "mol cyclegan a generative model for molecular optimization"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "graph memory networks for molecular activity prediction"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"

Connected through hyperedge HE-4 (induced by source paper "gen highly efficient smiles explorer using autodidactic generative examination networks"):
  with members:
    - "deep learning for molecular design a review of the state of the art"
    - "from gan to wgan"
    - "a survey on deep transfer learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "recent trends in deep learning based natural language processing"
    - "in silico generation of novel drug like chemical matter using the lstm neural network"

Connected through hyperedge HE-5 (induced by source paper "learning deep generative models of graphs"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular de novo design through deep reinforcement learning"
    - "neural message passing for quantum chemistry"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "generating focussed molecule libraries for drug discovery with recurrent neural networks"
    - "gated graph sequence neural networks"
    - "recurrent neural network grammars"

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 "molecular generative model based on conditional variational autoencoder for de novo molecular design"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "junction tree variational autoencoder for molecular graph generation"
    - "molecular de novo design through deep reinforcement learning"
    - "application of generative autoencoder in de novo molecular design"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"

Connected through hyperedge HE-8 (induced by source paper "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "application of generative autoencoder in de novo molecular design"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

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)