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: "generating and designing dna with deep generative models"
  Abstract: We propose generative neural network methods to generate DNA sequences and tune them to have desired properties. We present three approaches: creating synthetic DNA sequences using a generative adversarial network; a DNA-based variant of the activation maximization ("deep dream") design method; and a joint procedure which combines these two approaches together. We show that these tools capture important structures of the data and, when applied to designing probes for protein binding microarrays, allow us to generate new sequences whose properties are estimated to be superior to those found in the training data. We believe that these results open the door for applying deep generative models to advance genomics research.

Connected through hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "face aging with conditional generative adversarial networks"
    - "logan membership inference attacks against generative models"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "understanding deep learning requires rethinking generalization"
    - "concentrated differential privacy"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "design by adaptive sampling"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "on calibration of modern neural networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "improved training of wasserstein gans"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "the cma evolution strategy a tutorial"
    - "adam a method for stochastic optimization"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)