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: "graph normalizing flows"
  Abstract: We introduce graph normalizing flows: a new, reversible graph neural network model for prediction and generation. On supervised tasks, graph normalizing flows perform similarly to message passing neural networks, but at a significantly reduced memory footprint, allowing them to scale to larger graphs. In the unsupervised case, we combine graph normalizing flows with a novel graph auto-encoder to create a generative model of graph structures. Our model is permutation-invariant, generating entire graphs with a single feed-forward pass, and achieves competitive results with the state-of-the art auto-regressive models, while being better suited to parallel computing architectures.

Connected through hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-2 (induced by source paper "graphaf a flow based autoregressive model for molecular graph generation"):
  with members:
    - "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"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-3 (induced by source paper "latent variable modelling with hyperbolic normalizing flows"):
  with members:
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "mixed curvature variational autoencoders"
    - "poincare wasserstein autoencoder"
    - "hyperbolic graph neural networks"
    - "hyperbolic image embeddings"
    - "hyperbolic graph convolutional neural networks"
    - "hyperbolic attention networks"

Connected through hyperedge HE-4 (induced by source paper "permutation invariant graph generation via score based generative modeling"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "generative modeling by estimating gradients of the data distribution"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "exploring randomly wired neural networks for image recognition"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "representation learning on graphs with jumping knowledge networks"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"

Connected through hyperedge HE-5 (induced by source paper "gfcn a new graph convolutional network based on parallel flows"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "topology adaptive graph convolutional networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "graph learning from data under laplacian and structural constraints"
    - "graph based convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "continuous graph flow"):
  with members:
    - "layoutvae stochastic scene layout generation from a label set"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "image generation from scene graphs"
    - "image generation from layout"
    - "situation recognition with graph neural networks"

Connected through hyperedge HE-7 (induced by source paper "graph residual flow for molecular graph generation"):
  with members:
    - "on asymptotic behaviors of graph cnns from dynamical systems perspective"
    - "mintnet building invertible neural networks with masked convolutions"
    - "simplifying graph convolutional 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"
    - "spectral normalization for generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-8 (induced by source paper "graph generation with variational recurrent neural network"):
  with members:
    - "a variational auto encoder model for stochastic point processes"
    - "representation learning on graphs a reinforcement learning application"
    - "on the necessity and effectiveness of learning the prior of variational auto encoder"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "density estimation using real nvp"

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)