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: "application of generative autoencoder in de novo molecular design"
  Abstract: A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used to map molecule structures into a continuous latent space and vice versa and their performance as structure generator was assessed. Our results show that the latent space preserves chemical similarity principle and thus can be used for the generation of analogue structures. Furthermore, the latent space created by autoencoders were searched systematically to generate novel compounds with predicted activity against dopamine receptor type 2 and compounds similar to known active compounds not included in the training set were identified.

Connected through hyperedge HE-1 (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-2 (induced by source paper "likelihood free inference and generation of molecular graphs"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep reinforcement learning for de novo drug design"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-3 (induced by source paper "toward explainable anticancer compound sensitivity prediction via multimodal attention based convolutional encoders"):
  with members:
    - "paccmann prediction of anticancer compound sensitivity with multi modal attention based neural networks"
    - "deep reinforcement learning for de novo drug design"
    - "synergy effect between convolutional neural networks and the multiplicity of smiles for improvement of molecular prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "a hybrid deep learning approach for diagnosis of the erythemato squamous disease"):
  with members:
    - "care2vec a deep learning approach for the classification of self care problems in physically disabled children"
    - "xgboost a scalable tree boosting system"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "molecular sets moses a benchmarking platform for molecular generation models"):
  with members:
    - "conditional molecular design with deep generative models"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "deep reinforcement learning for de novo drug design"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "junction tree variational autoencoder for molecular graph generation"
    - "syntax directed variational autoencoder for structured data"

Connected through hyperedge HE-6 (induced by source paper "optimization of molecules via deep reinforcement learning"):
  with members:
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "deep reinforcement learning for de novo drug design"
    - "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 generative model based on conditional variational autoencoder for de novo molecular design"
    - "molecular de novo design through deep reinforcement learning"

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"
    - "molecular generation with recurrent neural networks rnns"
    - "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"
    - "molecular generation with recurrent neural networks rnns"
    - "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)