human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning universal graph neural network embeddings with aid of transfer learning"):
  Abstract: Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning. However currently in the graph learning domain, embeddings learned through existing graph neural networks (GNNs) are task dependent and thus cannot be shared across different datasets. In this paper, we present a first powerful and theoretically guaranteed graph neural network that is designed to learn task-independent graph embeddings, thereafter referred to as deep universal graph embedding (DUGNN). Our DUGNN model incorporates a novel graph neural network (as a universal graph encoder) and leverages rich Graph Kernels (as a multi-task graph decoder) for both unsupervised learning and (task-specific) adaptive supervised learning. By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning. Through extensive experiments and ablation studies, we show that the proposed DUGNN model consistently outperforms both the existing state-of-art GNN models and Graph Kernels by an increased accuracy of 3% - 8% on graph classification benchmark datasets.

Includes members:
  Member "stability and generalization of graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalization and representational limits of graph neural networks")
  Member "graph convolutional policy network for goal directed molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "can graph neural networks count substructures")
  Member "adaptive graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "the natural language decathlon multitask learning as question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "graphvae towards generation of small graphs using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "hierarchical graph representation learning with differentiable pooling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based graph networks")
  Member "deeper insights into graph convolutional networks for semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "memory based graph networks")
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")

Question: Please tell me which class the source paper 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)