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 neural networks via node feature propagation"
  Abstract: Graph Neural Networks (GNNs) for prediction tasks like node classification or edge prediction have received increasing attention in recent machine learning from graphically structured data. However, a large quantity of labeled graphs is difficult to obtain, which significantly limits the true success of GNNs. Although active learning has been widely studied for addressing label-sparse issues with other data types like text, images, etc., how to make it effective over graphs is an open question for research. In this paper, we present an investigation on active learning with GNNs for node classification tasks. Specifically, we propose a new method, which uses node feature propagation followed by K-Medoids clustering of the nodes for instance selection in active learning. With a theoretical bound analysis we justify the design choice of our approach. In our experiments on four benchmark datasets, the proposed method outperforms other representative baseline methods consistently and significantly.

Connected through hyperedge HE-1 (induced by source paper "active learning for entity alignment"):
  with members:
    - "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"
    - "active learning for graph 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 "adversarial model extraction on graph neural networks"):
  with members:
    - "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"
    - "active learning for graph embedding"
    - "stealing machine learning models via prediction apis"

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)