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: "graphvae towards generation of small graphs using variational autoencoders"
  Abstract: Deep learning on graphs has become a popular research topic with many applications. However, past work has concentrated on learning graph embedding tasks, which is in contrast with advances in generative models for images and text. Is it possible to transfer this progress to the domain of graphs? We propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fully-connected graph of a predefined maximum size directly at once. Our method is formulated as a variational autoencoder. We evaluate on the challenging task of molecule generation.

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"
    - "hierarchical graph representation learning with differentiable pooling"
    - "optimization of molecules via deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "hierarchical video frame sequence representation with deep convolutional graph network"
    - "tvqa localized compositional video question answering"
    - "attention is all you need"
    - "neural message passing for quantum chemistry"
    - "vain attentional multi agent predictive modeling"
    - "convolutional sequence to sequence learning"
    - "one shot imitation learning"

Connected through hyperedge HE-3 (induced by source paper "simple and effective graph autoencoders with one hop linear models"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "a degeneracy framework for scalable graph autoencoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "effective decoding in graph auto encoder using triadic closure"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "are we really making much progress a worrying analysis of recent neural recommendation approaches"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a deep generative model for graph layout"):
  with members:
    - "evaluating the readability of force directed graph layouts a deep learning approach"
    - "on the convergence of adam and beyond"
    - "aesthetic discrimination of graph layouts"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"

Connected through hyperedge HE-6 (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"
    - "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-7 (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"
    - "graph normalizing flows"
    - "exploring randomly wired neural networks for image recognition"
    - "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-8 (induced by source paper "graph generators state of the art and open challenges"):
  with members:
    - "a survey on theoretical advances of community detection in networks"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "gmark schema driven generation of graphs and queries"
    - "motifs in temporal networks"
    - "g core a core for future graph query languages"
    - "darwini generating realistic large scale social graphs"
    - "an introduction to temporal graphs an algorithmic perspective"

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)