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: "convolutional neural networks via node varying graph filters"
  Abstract: Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs. The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. A second contribution is to replace the node-varying GF with a hybrid node-varying GF, which is a new type of GF introduced in this paper. While the alternative architecture can still be run locally without requiring a pooling stage, the number of trainable parameters is smaller and can be rendered independent of the data dimension. Tests are run on a synthetic source localization problem and on the 20NEWS dataset.

Connected through hyperedge HE-1 (induced by source paper "edgenets edge varying graph neural networks"):
  with members:
    - "dfnets spectral cnns for graphs with feedback looped filters"
    - "graph neural networks with convolutional arma filters"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "generalizing graph convolutional neural networks with edge variant recursions on graphs"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "all graphs lead to rome learning geometric and cycle consistent representations with graph convolutional networks"):
  with members:
    - "group normalization"
    - "relational inductive biases deep learning and graph networks"
    - "mimo graph filters for convolutional neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to find good correspondences"
    - "multi image semantic matching by mining consistent features"
    - "lift learned invariant feature transform"

Connected through hyperedge HE-3 (induced by source paper "generalizing graph convolutional neural networks with edge variant recursions on graphs"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "topology adaptive graph convolutional networks"
    - "median activation functions for graph neural networks"
    - "autoregressive moving average graph filtering"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "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-4 (induced by source paper "mimo graph filters for convolutional neural networks"):
  with members:
    - "topology adaptive graph convolutional networks"
    - "convolutional neural networks on irregular domains through approximate translations on inferred graphs"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "improving graph convolutional networks with non parametric activation functions"):
  with members:
    - "neural graph machines learning neural networks using graphs"
    - "geometric deep learning going beyond euclidean data"
    - "learning to represent programs with graphs"
    - "adaptive least mean squares estimation of graph signals"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "semi supervised learning with ladder networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-6 (induced by source paper "a recurrent graph neural network for multi relational data"):
  with members:
    - "median activation functions for graph neural networks"
    - "geometric deep learning going beyond euclidean data"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "sampling of graph signals with successive local aggregations"
    - "tensorflow a system for large scale machine learning"

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)