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: "d vae a variational autoencoder for directed acyclic graphs"
  Abstract: Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study deep generative models for DAGs, and propose a novel DAG variational autoencoder (D-VAE). To encode DAGs into the latent space, we leverage graph neural networks. We propose an asynchronous message passing scheme that allows encoding the computations on DAGs, rather than using existing simultaneous message passing schemes to encode local graph structures. We demonstrate the effectiveness of our proposed DVAE through two tasks: neural architecture search and Bayesian network structure learning. Experiments show that our model not only generates novel and valid DAGs, but also produces a smooth latent space that facilitates searching for DAGs with better performance through Bayesian optimization.

Connected through hyperedge HE-1 (induced by source paper "graphgen a scalable approach to domain agnostic labeled graph generation"):
  with members:
    - "deepnc deep generative network completion"
    - "labeled graph generative adversarial networks"
    - "molecularrnn generating realistic molecular graphs with optimized properties"
    - "gram scalable generative models for graphs with graph attention mechanism"
    - "efficient graph generation with graph recurrent attention networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained graph variational autoencoders for molecule design"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-2 (induced by source paper "bananas bayesian optimization with neural architectures for neural architecture search"):
  with members:
    - "probo a framework for using probabilistic programming in bayesian optimization"
    - "multi objective neural architecture search via predictive network performance optimization"
    - "random search and reproducibility for neural architecture search"
    - "probo versatile bayesian optimization using any probabilistic programming language"
    - "sample efficient neural architecture search by learning action space"
    - "nas bench 101 towards reproducible neural architecture search"
    - "bayesnas a bayesian approach for neural architecture search"
    - "evaluating the search phase of neural architecture search"

Connected through hyperedge HE-3 (induced by source paper "a variational sequential graph autoencoder for neural architecture performance prediction"):
  with members:
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "neural architecture optimization"
    - "learning deep generative models of graphs"

Connected through hyperedge HE-4 (induced by source paper "gravity inspired graph autoencoders for directed link prediction"):
  with members:
    - "a degeneracy framework for scalable graph autoencoders"
    - "simplifying graph convolutional networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "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"
    - "learning to make predictions on graphs with autoencoders"
    - "adversarially regularized graph autoencoder for graph embedding"

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)