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: "a degeneracy framework for scalable graph autoencoders"
  Abstract: In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective propagation mechanism, our approach significantly improves scalability and training speed while preserving performance. We evaluate and discuss our method on several variants of existing graph AE and VAE, providing the first application of these models to large graphs with up to millions of nodes and edges. We achieve empirically competitive results w.r.t. several popular scalable node embedding methods, which emphasizes the relevance of pursuing further research towards more scalable graph AE and VAE.

Connected through hyperedge HE-1 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding"):
  with members:
    - "simple and effective graph autoencoders with one hop linear models"
    - "gravity inspired graph autoencoders for directed link prediction"
    - "effective decoding in graph auto encoder using triadic closure"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semi implicit graph variational auto encoders"
    - "improved training speed accuracy and data utilization through loss function optimization"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"

Connected through hyperedge HE-2 (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"
    - "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"
    - "constrained graph variational autoencoders for molecule design"

Connected through hyperedge HE-3 (induced by source paper "bipartite link prediction based on topological features via 2 hop path"):
  with members:
    - "rwr gae random walk regularization for graph auto encoders"
    - "keep it simple graph autoencoders without graph convolutional networks"
    - "bipartite graph neural networks for efficient node representation learning"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "gravity inspired graph autoencoders for directed link prediction"):
  with members:
    - "d vae a variational autoencoder for directed acyclic graphs"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "keep it simple graph autoencoders without graph convolutional networks"):
  with members:
    - "gravity inspired graph autoencoders for directed link prediction"
    - "joint embedding of structure and features via graph convolutional networks"
    - "rwr gae random walk regularization for graph auto encoders"
    - "simplifying graph convolutional networks"
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "a comprehensive survey on graph neural networks"
    - "matrix completion with variational graph autoencoders application in hyperlocal air quality inference"
    - "constrained graph variational autoencoders for molecule design"

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)