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: "attributed graph clustering a deep attentional embedding approach"
  Abstract: Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compact graph embedding, upon which classic clustering methods like k-means or spectral clustering algorithms are applied. These two-step frameworks are difficult to manipulate and usually lead to suboptimal performance, mainly because the graph embedding is not goal-directed, i.e., designed for the specific clustering task. In this paper, we propose a goal-directed deep learning approach, Deep Attentional Embedded Graph Clustering (DAEGC for short). Our method focuses on attributed graphs to sufficiently explore the two sides of information in graphs. By employing an attention network to capture the importance of the neighboring nodes to a target node, our DAEGC algorithm encodes the topological structure and node content in a graph to a compact representation, on which an inner product decoder is trained to reconstruct the graph structure. Furthermore, soft labels from the graph embedding itself are generated to supervise a self-training graph clustering process, which iteratively refines the clustering results. The self-training process is jointly learned and optimized with the graph embedding in a unified framework, to mutually benefit both components. Experimental results compared with state-of-the-art algorithms demonstrate the superiority of our method.

Connected through hyperedge HE-1 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning graph embedding with adversarial training methods"
    - "label efficient semi supervised learning via graph filtering"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "review of visual saliency detection with comprehensive information"
    - "iterative visual reasoning beyond convolutions"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "structural deep clustering network"):
  with members:
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "deep clustering for unsupervised learning of visual features"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "deep subspace clustering networks"
    - "semi supervised classification with graph convolutional networks"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "lstm based encoder decoder for multi sensor anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "adaptive graph auto encoder for general data clustering"):
  with members:
    - "symmetric graph convolutional autoencoder for unsupervised graph representation learning"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "how powerful are graph neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "learning convolutional neural networks for graphs"
    - "unsupervised deep embedding for clustering analysis"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-4 (induced by source paper "community detection in node attributed social networks a survey"):
  with members:
    - "non linear attributed graph clustering by symmetric nmf with pu learning"
    - "efficiently clustering very large attributed graphs"
    - "a survey on network embedding"
    - "heterogeneous information network embedding for meta path based proximity"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "mining application aware community organization with expanded feature subspaces from concerned attributes in social networks"
    - "node2vec scalable feature learning for networks"
    - "structure and inference in annotated networks"

Connected through hyperedge HE-5 (induced by source paper "prototype propagation networks ppn for weakly supervised few shot learning on category graph"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "a closer look at few shot classification"
    - "a comprehensive survey on graph neural networks"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "syntax directed variational autoencoder for structured data"
    - "attention is all you need"

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)