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: "tree based kernel for graphs with continuous attributes"
  Abstract: The availability of graph data with node attributes that can be either discrete or real-valued is constantly increasing. While existing Kernel methods are effective techniques for dealing with graphs having discrete node labels, their adaptation to nondiscrete or continuous node attributes has been limited, mainly for computational issues. Recently, a few kernels especially tailored for this domain, and that trade predictive performance for computational efficiency, have been proposed. In this brief, we propose a graph kernel for complex and continuous nodes’ attributes, whose features are tree structures extracted from specific graph visits. The kernel manages to keep the same complexity of the state-of-the-art kernels while implicitly using a larger feature space. We further present an approximated variant of the kernel, which reduces its complexity significantly. Experimental results obtained on six real-world data sets show that the kernel is the best performing one on most of them. Moreover, in most cases, the approximated version reaches comparable performances to the current state-of-the-art kernels in terms of classification accuracy while greatly shortening the running times.

Connected through hyperedge HE-1 (induced by source paper "fast haar transforms for graph neural networks"):
  with members:
    - "pan path integral based convolution for deep graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "haarpooling graph pooling with compressive haar basis"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "deeper insights into graph convolutional networks for semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "a unifying view of explicit and implicit feature maps of graph kernels"):
  with members:
    - "a survey on graph kernels"
    - "graph convolutional neural networks for web scale recommender systems"
    - "representation learning on graphs methods and applications"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"
    - "on valid optimal assignment kernels and applications to graph classification"

Connected through hyperedge HE-3 (induced by source paper "graph convolutional neural networks based on quantum vertex saliency"):
  with members:
    - "concept drift and anomaly detection in graph streams"
    - "how powerful are graph neural networks"
    - "anonymous walk embeddings"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "generalizing the convolution operator to extend cnns to irregular domains"

Connected through hyperedge HE-4 (induced by source paper "concept drift and anomaly detection in graph streams"):
  with members:
    - "one class classifiers based on entropic spanning graphs"
    - "modeling and measuring graph similarity the case for centrality distance"
    - "detecting change points in the large scale structure of evolving networks"
    - "graph based anomaly detection and description a survey"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-5 (induced by source paper "pre training graph neural networks with kernels"):
  with members:
    - "on filter size in graph convolutional networks"
    - "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)