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: "defactor differentiable edge factorization based probabilistic graph generation"
  Abstract: Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of performing de-novo molecular design. Although graph generative models are currently available they either have a graph size dependency in their number of parameters, limiting their use to only very small graphs or are formulated as a sequence of discrete actions needed to construct a graph, making the output graph non-differentiable w.r.t the model parameters, therefore preventing them to be used in scenarios such as conditional graph generation. In this work we propose a model for conditional graph generation that is computationally efficient and enables direct optimisation of the graph. We demonstrate favourable performance of our model on prototype-based molecular graph conditional generation tasks.

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 "towards interpretable sparse graph representation learning with laplacian pooling"):
  with members:
    - "graph convolutional networks with eigenpooling"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-3 (induced by source paper "data driven approach to encoding and decoding 3 d crystal structures"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "tackling climate change with machine learning"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "generating diverse high fidelity images with vq vae 2"
    - "generative models for automatic chemical design"
    - "crystalgan learning to discover crystallographic structures with generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-4 (induced by source paper "a model to search for synthesizable molecules"):
  with members:
    - "constrained graph variational autoencoders for molecule design"
    - "molecular hypergraph grammar with its application to molecular optimization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "study of deep generative models for inorganic chemical compositions"):
  with members:
    - "preventing posterior collapse with delta vaes"
    - "generative models for automatic chemical design"
    - "a two step graph convolutional decoder for molecule generation"
    - "data driven approach to encoding and decoding 3 d crystal structures"
    - "crystalgan learning to discover crystallographic structures with generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "learning latent subspaces in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-6 (induced by source paper "augmenting genetic algorithms with deep neural networks for exploring the chemical space"):
  with members:
    - "selfies a robust representation of semantically constrained graphs with an example application in chemistry"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "learning multimodal graph to graph translation for molecular optimization"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular sets moses a benchmarking platform for molecular generation models"
    - "junction tree variational autoencoder for molecular 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)