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: "unifying local and non local signal processing with graph cnns"
  Abstract: This paper deals with the unification of local and non-local signal processing on graphs within a single convolutional neural network (CNN) framework. Building upon recent works on graph CNNs, we propose to use convolutional layers that take as inputs two variables, a signal and a graph, allowing the network to adapt to changes in the graph structure. In this article, we explain how this framework allows us to design a novel method to perform style transfer.

Connected through hyperedge HE-1 (induced by source paper "learning universal graph neural network embeddings with aid of transfer learning"):
  with members:
    - "stability and generalization of graph convolutional neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "adaptive graph convolutional neural networks"
    - "the natural language decathlon multitask learning as question answering"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "graphrnn generating realistic graphs with deep auto regressive models"

Connected through hyperedge HE-2 (induced by source paper "stability and generalization of graph convolutional neural networks"):
  with members:
    - "adaptive graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "how powerful are graph neural networks"
    - "exploring graph structured passage representation for multi hop reading comprehension with graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "deriving neural architectures from sequence and graph kernels"
    - "exploring generalization in deep learning"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"

Connected through hyperedge HE-3 (induced by source paper "audio style transfer"):
  with members:
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep photo style transfer"
    - "neural style transfer a review"
    - "texture synthesis using shallow convolutional networks with random filters"
    - "a powerful generative model using random weights for the deep image representation"
    - "wavenet a generative model for raw audio"
    - "soundnet learning sound representations from unlabeled video"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "fast spectral ranking for similarity search"):
  with members:
    - "geometric deep learning going beyond euclidean data"
    - "end to end learning of deep visual representations for image retrieval"
    - "deep image retrieval learning global representations for image search"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "efficient diffusion on region manifolds recovering small objects with compact cnn representations"
    - "fast exact and multi scale inference for semantic image segmentation with deep gaussian crfs"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"

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.CV(Computer Vision and Pattern Recognition)