human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graph convolutional auto encoder with bi decoder and adaptive sharing adjacency"):
  Abstract: Graph autoencoder (GAE) serves as an effective unsupervised learning framework to represent graph data in a latent space for network embedding. Most exiting approaches typically focus on minimizing the reconstruction loss of graph structure but neglect the reconstruction of node features, which may result in overfitting due to the capacity of the autoencoders. Additionally, the adjacency matrix in these methods is always fixed such that the adjacency matrix cannot properly represent the connections among nodes in latent space. To solve this problem, in this paper, we propose a novel Graph Convolutional Auto-encoder with Bidecoder and Adaptive-sharing Adjacency method, namely BAGA. The framework encodes the topological structure and node features into latent representations, on which a bi-decoder is trained to reconstruct the graph structure and node features simultaneously. Furthermore, the adjacency matrix can be adaptively updated by the learned latent representations for better representing the connections among nodes in latent space. Experimental results on datasets validate the superiority of our method to the state-of-the-art network embedding methods on the clustering task.

Includes members:
  Member "attributed graph clustering via adaptive graph convolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self supervised graph representation learning via global context prediction")
  Member "a comprehensive survey on graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "learning deep generative models of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "adversarially regularized graph autoencoder for graph embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fastgae fast scalable and effective graph autoencoders with stochastic subgraph decoding")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep graph matching consensus")
  Member "semi supervised classification with graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "deepwalk online learning of social representations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")

Question: Please tell me which class the source paper 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)