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: "designing random graph models using variational autoencoders with applications to chemical design"
  Abstract: Deep generative models have been praised for their ability to learn smooth latent representation of images, text, and audio, which can then be used to generate new, plausible data. However, current generative models are unable to work with graphs due to their unique characteristics--their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes labels, and they come with a different number of nodes and edges. In this paper, we propose a variational autoencoder for graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, the decoder is able to guarantee a set of local structural and functional properties in the generated graphs. Experiments reveal that our model is able to learn and mimic the generative process of several well-known random graph models and can be used to create new molecules more effectively than several state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "hierarchical generation of molecular graphs using structural motifs"):
  with members:
    - "discrete object generation with reversible inductive construction"
    - "graph u nets"
    - "conditional molecular design with deep generative models"
    - "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"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (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"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "graphaf a flow based autoregressive model for molecular graph generation"):
  with members:
    - "graph normalizing flows"
    - "molecularrnn generating realistic molecular graphs with optimized properties"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "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"
    - "molecular sets moses a benchmarking platform for molecular generation models"

Connected through hyperedge HE-4 (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-5 (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-6 (induced by source paper "all smiles variational autoencoder"):
  with members:
    - "conditional molecular design with deep generative models"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders"
    - "potentialnet for molecular property prediction"
    - "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"
    - "deeply learning molecular structure property relationships using attention and gate augmented graph convolutional network"

Connected through hyperedge HE-7 (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-8 (induced by source paper "selfies a robust representation of semantically constrained graphs with an example application in chemistry"):
  with members:
    - "deep learning for molecular design a review of the state of the art"
    - "interpretable deep learning in drug discovery"
    - "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"
    - "deep reinforcement learning for de novo drug design"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning deep generative models of graphs"

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)