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: "a unifying view of explicit and implicit feature maps for structured data systematic studies of graph kernels"
  Abstract: Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. To this end, explicit feature maps of kernels for vectorial data have been extensively studied. As many real-world data is structured, various kernels for complex data like graphs have been proposed. Indeed, many of them directly compute feature maps. However, the kernel trick is employed when the number of features is very large or the individual vertices of graphs are annotated by real-valued attributes. #R##N#Can we still compute explicit feature maps efficiently under these circumstances? Triggered by this question, we investigate how general convolution kernels are composed from base kernels and construct corresponding feature maps. We apply our results to widely used graph kernels and analyze for which kernels and graph properties computation by explicit feature maps is feasible and actually more efficient. In particular, we derive feature maps for random walk and subgraph matching kernels and apply them to real-world graphs with discrete labels. Thereby, our theoretical results are confirmed experimentally by observing a phase transition when comparing running time with respect to label diversity, walk lengths and subgraph size, respectively. Moreover, we derive approximative, explicit feature maps for state-of-the-art kernels supporting real-valued attributes including the GraphHopper and Graph Invariant kernels. In extensive experiments we show that our approaches often achieve a classification accuracy close to the exact methods based on the kernel trick, but require only a fraction of their running time.

Connected through hyperedge HE-1 (induced by source paper "graph convolutional networks with eigenpooling"):
  with members:
    - "graph u nets"
    - "graph neural networks for social recommendation"
    - "graph representation learning via hard and channel wise attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-2 (induced by source paper "rethinking kernel methods for node representation learning on graphs"):
  with members:
    - "construct dynamic graphs for hand gesture recognition via spatial temporal attention"
    - "cluster gcn an efficient algorithm for training deep and large graph convolutional networks"
    - "semantic graph convolutional networks for 3d human pose regression"
    - "graph matching networks for learning the similarity of graph structured objects"
    - "quantized densely connected u nets for efficient landmark localization"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "learning to forecast and refine residual motion for image to video generation"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-3 (induced by source paper "temporal graph kernels for classifying dissemination processes"):
  with members:
    - "a survey on graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "time series prediction for graphs in kernel and dissimilarity spaces"
    - "neural message passing for quantum chemistry"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "an introduction to temporal graphs an algorithmic perspective"

Connected through hyperedge HE-4 (induced by source paper "graph classification with 2d convolutional neural networks"):
  with members:
    - "attributed social network embedding"
    - "node2vec scalable feature learning for networks"
    - "densely connected convolutional networks"
    - "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"
    - "faster kernels for graphs with continuous attributes via hashing"

Connected through hyperedge HE-5 (induced by source paper "a multi view context aware approach to android malware detection and malicious code localization"):
  with members:
    - "poisoning behavioral malware clustering"
    - "contextual weisfeiler lehman graph kernel for malware detection"
    - "mamadroid detecting android malware by building markov chains of behavioral models"
    - "adaptive and scalable android malware detection through online learning"
    - "why should i trust you explaining the predictions of any classifier"
    - "subgraph2vec learning distributed representations of rooted sub graphs from large graphs"

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)