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: "multi task graph autoencoders"
  Abstract: We examine two fundamental tasks associated with graph representation learning: link prediction and node classification. We present a new autoencoder architecture capable of learning a joint representation of local graph structure and available node features for the simultaneous multi-task learning of unsupervised link prediction and semi-supervised node classification. Our simple, yet effective and versatile model is efficiently trained end-to-end in a single stage, whereas previous related deep graph embedding methods require multiple training steps that are difficult to optimize. We provide an empirical evaluation of our model on five benchmark relational, graph-structured datasets and demonstrate significant improvement over three strong baselines for graph representation learning. Reference code and data are available at this https URL

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"
    - "a degeneracy framework for scalable graph autoencoders"
    - "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"

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"
    - "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-3 (induced by source paper "fast evaluation of link prediction by random sampling of unobserved links"):
  with members:
    - "gcn gan a non linear temporal link prediction model for weighted dynamic networks"
    - "link prediction based on graph neural networks"
    - "link prediction in dynamic graphs for recommendation"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "end to end structure aware convolutional networks for knowledge base completion"
    - "gemsec graph embedding with self clustering"
    - "scalable temporal latent space inference for link prediction in dynamic social networks"
    - "performance of local information based link prediction a sampling perspective"

Connected through hyperedge HE-4 (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"
    - "a degeneracy framework for scalable graph autoencoders"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "graph star net for generalized multi task learning"):
  with members:
    - "simplifying graph convolutional networks"
    - "star transformer"
    - "unsupervised data augmentation for consistency training"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "graph convolutional networks for text classification"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"

Connected through hyperedge HE-6 (induced by source paper "joint embedding of structure and features via graph convolutional networks"):
  with members:
    - "location occupation and semantics based socioeconomic status inference on twitter"
    - "graph convolutional neural networks for web scale recommender systems"
    - "socioeconomic dependencies of linguistic patterns in twitter a multivariate analysis"
    - "modeling polypharmacy side effects with graph convolutional networks"
    - "flexible attributed network embedding"
    - "geometric deep learning going beyond euclidean data"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "a comprehensive survey of graph embedding problems techniques and applications"

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)