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: "shift aggregate extract networks"
  Abstract: We introduce an architecture based on deep hierarchical decompositions to learn effective representations of large graphs. Our framework extends classic R-decompositions used in kernel methods, enabling nested "part-of-part" relations. Unlike recursive neural networks, which unroll a template on input graphs directly, we unroll a neural network template over the decomposition hierarchy, allowing us to deal with the high degree variability that typically characterize social network graphs. Deep hierarchical decompositions are also amenable to domain compression, a technique that reduces both space and time complexity by exploiting symmetries. We show empirically that our approach is competitive with current state-of-the-art graph classification methods, particularly when dealing with social network datasets.

Connected through hyperedge HE-1 (induced by source paper "graph inference learning for semi supervised classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "topology adaptive graph convolutional networks"
    - "learning to learn meta critic networks for sample efficient learning"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-2 (induced by source paper "rep the set neural networks for learning set representations"):
  with members:
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep sets"
    - "so net self organizing network for point cloud analysis"
    - "struc2vec learning node representations from structural identity"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning convolutional neural networks for graphs"
    - "deepsetnet predicting sets with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "multi hop assortativities for network classification"):
  with members:
    - "learning convolutional neural networks for graphs"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "walk steered convolution for graph classification"):
  with members:
    - "gaussian induced convolution for graphs"
    - "graph embedding techniques applications and performance a survey"
    - "temporal segment networks for action recognition in videos"
    - "representation learning on graphs methods and applications"
    - "robust spatial filtering with graph convolutional neural networks"
    - "learning graph representations with embedding propagation"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "gaussian induced convolution for graphs"):
  with members:
    - "context dependent diffusion network for visual relationship detection"
    - "tensor graph convolutional neural network"
    - "action attending graphic neural network"
    - "when work matters transforming classical network structures to graph cnn"
    - "walk steered convolution for graph classification"
    - "spatio temporal graph convolution for skeleton based action recognition"
    - "spectral filter tracking"
    - "learning graph representations with embedding propagation"

Connected through hyperedge HE-6 (induced by source paper "when work matters transforming classical network structures to graph cnn"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "mixed link networks"
    - "dgcnn disordered graph convolutional neural network based on the gaussian mixture model"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "densely connected convolutional networks"
    - "time variant graph classification"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"

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)