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: "topology adaptive graph convolutional networks"
  Abstract: Convolution acts as a local feature extractor in convolutional neural networks (CNNs). However, the convolution operation is not applicable when the input data is supported on an irregular graph such as with social networks, citation networks, or knowledge graphs. This paper proposes the topology adaptive graph convolutional network (TAGCN), a novel graph convolutional network that generalizes CNN architectures to graph-structured data and provides a systematic way to design a set of fixed-size learnable filters to perform convolutions on graphs. The topologies of these filters are adaptive to the topology of the graph when they scan the graph to perform convolution, replacing the square filter for the grid-structured data in traditional CNNs. The outputs are the weighted sum of these filters’ outputs, extraction of both vertex features and strength of correlation between vertices. It can be used with both directed and undirected graphs. The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing. Further, as no approximation to the convolution is needed, TAGCN exhibits better performance than existing graph-convolution-approximation methods on a number of data sets. As only the polynomials of degree two of the adjacency matrix are used, TAGCN is also computationally simpler than other recent methods.

Connected through hyperedge HE-1 (induced by source paper "std net structure preserving and topology adaptive deformation network for 3d reconstruction from a single image"):
  with members:
    - "structurenet hierarchical graph networks for 3d shape generation"
    - "geometrics exploiting geometric structure for graph encoded objects"
    - "3dn 3d deformation network"
    - "sdm net deep generative network for structured deformable mesh"
    - "learning category specific mesh reconstruction from image collections"
    - "im2struct recovering 3d shape structure from a single rgb image"
    - "3d coded 3d correspondences by deep deformation"
    - "pixel2mesh generating 3d mesh models from single rgb images"

Connected through hyperedge HE-2 (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"
    - "learning to learn meta critic networks for sample efficient learning"
    - "shift aggregate extract networks"
    - "scene graph generation by iterative message passing"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-3 (induced by source paper "learning graph embedding with limited labeled data an efficient sampling approach"):
  with members:
    - "graph transformer networks"
    - "simplifying graph convolutional networks"
    - "pre training graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "sampling online social networks by random walk with indirect jumps"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional networks on graphs for learning molecular fingerprints"

Connected through hyperedge HE-4 (induced by source paper "gfcn a new graph convolutional network based on parallel flows"):
  with members:
    - "graph normalizing flows"
    - "adaptive graph convolutional neural networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "robust spatial filtering with graph convolutional neural networks"
    - "graph learning from data under laplacian and structural constraints"
    - "graph based convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "graphflow a new graph convolutional network based on parallel flows"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "on the universality of jordan centers for estimating infection sources in tree networks"
    - "predicting multicellular function through multi layer tissue networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "graph learning from data under laplacian and structural constraints"
    - "graph based convolutional neural network"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"

Connected through hyperedge HE-6 (induced by source paper "forecaster a graph transformer for forecasting spatial and time dependent data"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "image transformer"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "non local neural networks"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "generalizing graph convolutional neural networks with edge variant recursions on graphs"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "median activation functions for graph neural networks"
    - "autoregressive moving average graph filtering"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks via node varying graph filters"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-8 (induced by source paper "a unified deep learning formalism for processing graph signals"):
  with members:
    - "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)