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 hypergraph grammar with its application to molecular optimization"
  Abstract: This paper is concerned with a molecular optimization framework using variational autoencoders (VAEs). In this paradigm, VAE allows us to convert a molecular graph into/from its latent continuous vector, and therefore, the molecular optimization problem can be solved by continuous optimization techniques. One of the longstanding issues in this area is that it is difficult to always generate valid molecules. The very recent work called the junction tree variational autoencoder (JT-VAE) successfully solved this issue by generating a molecule fragment-by-fragment. While it achieves the state-of-the-art performance, it requires several neural networks to be trained, which predict which atoms are used to connect fragments and stereochemistry of each bond. In this paper, we present a molecular hypergraph grammar variational autoencoder (MHG-VAE), which uses a single VAE to address the issue. Our idea is to develop a novel graph grammar for molecular graphs called molecular hypergraph grammar (MHG), which can specify the connections between fragments and the stereochemistry on behalf of neural networks. This capability allows us to address the issue using only a single VAE. We empirically demonstrate the effectiveness of MHG-VAE over existing methods.

Connected through hyperedge HE-1 (induced by source paper "composing molecules with multiple property constraints"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "discrete object generation with reversible inductive construction"
    - "conditional molecular design with deep generative models"
    - "gnnexplainer generating explanations for graph neural networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "hypergraphs an introduction and review"):
  with members:
    - "networks of collaborations hypergraph modeling and visualisation"
    - "hypergraph covering problems motivated by genome assembly questions"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "a model to search for synthesizable molecules"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning deep generative models of graphs"
    - "molecular sets moses a benchmarking platform for molecular generation models"
    - "planning chemical syntheses with deep neural networks and symbolic ai"
    - "relational inductive biases deep learning and graph networks"
    - "defactor differentiable edge factorization based probabilistic graph generation"

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)