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: "on filter size in graph convolutional networks"
  Abstract: Recently, many researchers have been focusing on the definition of neural networks for graphs. The basic component for many of these approaches remains the graph convolution idea proposed almost a decade ago. In this paper, we extend this basic component, following an intuition derived from the well-known convolutional filters over multi-dimensional tensors. In particular, we derive a simple, efficient and effective way to introduce a hyper-parameter on graph convolutions that influences the filter size, i.e. its receptive field over the considered graph. We show with experimental results on real-world graph datasets that the proposed graph convolutional filter improves the predictive performance of Deep Graph Convolutional Networks.

Connected through hyperedge HE-1 (induced by source paper "dagcn dual attention graph convolutional networks"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "adaptive graph convolutional neural networks"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "watch your step learning node embeddings via graph attention"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"

Connected through hyperedge HE-2 (induced by source paper "structure fusion based on graph convolutional networks for semi supervised classification"):
  with members:
    - "transfer feature generating networks with semantic classes structure for zero shot learning"
    - "semi supervised learning on graph with an alternating diffusion process"
    - "multi gcn graph convolutional networks for multi view networks with applications to global poverty"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "class label autoencoder for zero shot learning"
    - "large scale learnable graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "fast and deep graph neural networks"):
  with members:
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "how powerful are graph neural networks"
    - "kernel graph convolutional neural networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "diffusion convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "pre training graph neural networks with kernels"):
  with members:
    - "tree based kernel for graphs with continuous attributes"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "bridging the gaps between residual learning recurrent neural networks and visual cortex"

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)