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: "spatially coupled neural network architectures"
  Abstract: In this work, we leverage advances in sparse coding techniques to reduce the number of trainable parameters in a fully connected neural network. While most of the works in literature impose $\ell_1$ regularization, DropOut or DropConnect techniques to induce sparsity, our scheme considers feature importance as a criterion to allocate the trainable parameters (resources) efficiently in the network. Even though sparsity is ensured, $\ell_1$ regularization requires training on all the resources in a deep neural network. The DropOut/DropConnect techniques reduce the number of trainable parameters in the training stage by dropping a random collection of neurons/edges in the hidden layers. However, both these techniques do not pay heed to the underlying structure in the data when dropping the neurons/edges. Moreover, these frameworks require a storage space equivalent to the number of parameters in a fully connected neural network. We address the above issues with a more structured architecture inspired from spatially-coupled sparse constructions. The proposed architecture is shown to have a performance akin to a conventional fully connected neural network with dropouts, and yet achieving a $94\%$ reduction in the training parameters. Extensive simulations are presented and the performance of the proposed scheme is compared against traditional neural network architectures.

Connected through hyperedge HE-1 (induced by source paper "semi implicit graph variational auto encoders"):
  with members:
    - "link prediction based on graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)