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 "attentional multilabel learning over graphs a message passing approach"):
  Abstract: We address a largely open problem of multilabel classification over graphs. Unlike traditional vector input, a graph has rich variable-size substructures which are related to the labels in some ways. We believe that uncovering these relations might hold the key to classification performance and explainability. We introduce GAML (Graph Attentional Multi-Label learning), a novel graph neural network that can handle this problem effectively. GAML regards labels as auxiliary nodes and models them in conjunction with the input graph. By applying message passing and attention mechanisms to both the label nodes and the input nodes iteratively, GAML can capture the relations between the labels and the input subgraphs at various resolution scales. Moreover, our model can take advantage of explicit label dependencies. It also scales linearly with the number of labels and graph size thanks to our proposed hierarchical attention. We evaluate GAML on an extensive set of experiments with both graph-structured inputs and classical unstructured inputs. The results show that GAML significantly outperforms other competing methods. Importantly, GAML enables intuitive visualizations for better understanding of the label-substructure relations and explanation of the model behaviors.

Includes members:
  Member "graph memory networks for molecular activity prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a regularized attention mechanism for graph attention networks")
  Member "junction tree variational autoencoder for molecular graph generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "benchmarking graph neural networks")
  Member "network representation learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches")
  Member "order free rnn with visual attention for multi label classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cross modality attention with semantic graph embedding for multi label classification")
  Member "representation learning on graphs methods and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to segment 3d point clouds in 2d image space")
  Member "graph convolutional networks for classification with a structured label space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "long tail relation extraction via knowledge graph embeddings and graph convolution networks")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "hcp a flexible cnn framework for multi label image classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")

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)