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: "in silico generation of novel drug like chemical matter using the lstm neural network"
  Abstract: The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2 hours. The molecules are novel, diverse (contain numerous novel chemotypes), have good physicochemical properties and have good synthetic accessibility, even though these qualities were not specific constraints. Although novel, their structural features and functional groups remain closely within the drug-like space defined by the bioactive molecules from ChEMBL. Virtual screening using the profile QSAR approach confirms that the potential of these novel molecules to show bioactivity is comparable to the ChEMBL set from which they were derived. The molecule generator written in Python used in this study is available on request.

Connected through hyperedge HE-1 (induced by source paper "generative models for automatic chemical design"):
  with members:
    - "selfies a robust representation of semantically constrained graphs with an example application in chemistry"
    - "a model to search for synthesizable molecules"
    - "all smiles variational autoencoder"
    - "likelihood free inference and generation of molecular graphs"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "labeled graph generative adversarial networks"
    - "decoding molecular graph embeddings with reinforcement learning"
    - "scaffold based molecular design using graph generative model"

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 "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 "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"
    - "molecular generation with recurrent neural networks rnns"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-5 (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-6 (induced by source paper "accelerating prototype based drug discovery using conditional diversity networks"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular de novo design through deep reinforcement learning"
    - "found in translation predicting outcomes of complex organic chemistry reactions using neural sequence to sequence models"
    - "moleculenet a benchmark for molecular machine learning"
    - "smiles enumeration as data augmentation for neural network modeling of molecules"
    - "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"

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)