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: "active learning for graph embedding"
  Abstract: Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph embedding algorithms assume the labelled nodes are given, which may not be always true in the real world. While manually label all training data is inapplicable, how to select the subset of training data to label so as to maximize the graph analysis task performance is of great importance. This motivates our proposed active graph embedding (AGE) framework, in which we design a general active learning query strategy for any semi-supervised graph embedding algorithm. AGE selects the most informative nodes as the training labelled nodes based on the graphical information (i.e., node centrality) as well as the learnt node embedding (i.e., node classification uncertainty and node embedding representativeness). Different query criteria are combined with the time-sensitive parameters which shift the focus from graph based query criteria to embedding based criteria as the learning progresses. Experiments have been conducted on three public data sets and the results verified the effectiveness of each component of our query strategy and the power of combining them using time-sensitive parameters. Our code is available online at: this https URL.

Connected through hyperedge HE-1 (induced by source paper "active learning for entity alignment"):
  with members:
    - "active learning for graph neural networks via node feature propagation"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "learning to exploit long term relational dependencies in knowledge graphs"
    - "lasagne locality and structure aware graph node embedding"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "deep bayesian active learning with image data"
    - "deep active learning over the long tail"

Connected through hyperedge HE-2 (induced by source paper "alpine active link prediction using network embedding"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "activehne active heterogeneous network embedding"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "deepwalk online learning of social representations"
    - "active learning on trees and graphs"
    - "active learning with statistical models"

Connected through hyperedge HE-3 (induced by source paper "active learning for graph neural networks via node feature propagation"):
  with members:
    - "simplifying graph convolutional networks"
    - "gradient descent finds global minima of deep neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "junction tree variational autoencoder for molecular graph generation"
    - "deep active learning for named entity recognition"
    - "neural message passing for quantum chemistry"
    - "a meta learning approach to one step active learning"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "activehne active heterogeneous network embedding"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "easing embedding learning by comprehensive transcription of heterogeneous information networks"
    - "local spectral graph convolution for point set feature learning"
    - "graph embedding techniques applications and performance a survey"
    - "aspem embedding learning by aspects in heterogeneous information networks"
    - "active discriminative text representation learning"
    - "a survey of heterogeneous information network analysis"

Connected through hyperedge HE-5 (induced by source paper "adversarial model extraction on graph neural networks"):
  with members:
    - "active learning for graph neural networks via node feature propagation"
    - "high fidelity extraction of neural network models"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "exploring connections between active learning and model extraction"
    - "how powerful are graph neural networks"
    - "stealing hyperparameters in machine learning"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-6 (induced by source paper "semi supervised adversarial active learning on attributed graphs"):
  with members:
    - "attributed network embedding via subspace discovery"
    - "semi supervised learning based on generative adversarial network a comparison between good gan and bad gan approach"
    - "good semi supervised learning that requires a bad gan"
    - "improved techniques for training gans"
    - "semi supervised classification with graph convolutional networks"
    - "towards principled unsupervised learning"
    - "deepwalk online learning of social representations"
    - "active semi supervised learning using submodular functions"

Connected through hyperedge HE-7 (induced by source paper "regional based query in graph active learning"):
  with members:
    - "activehne active heterogeneous network embedding"
    - "topological based classification of paper domains using graph convolutional networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "community detection in networks with node attributes"

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)