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 "spectral graph attention network"):
  Abstract: Variants of Graph Neural Networks (GNNs) for representation learning have been proposed recently and achieved fruitful results in various fields. Among them, graph attention networks (GATs) first employ a self-attention strategy to learn attention weights for each edge in the spatial domain. However, learning the attentions over edges only pays attention to the local information of graphs and greatly increases the number of parameters. In this paper, we first introduce attentions in the spectral domain of graphs. Accordingly, we present Spectral Graph Attention Network (SpGAT) that learn representations for different frequency components regarding weighted filters and graph wavelets bases. In this way, SpGAT can better capture global patterns of graphs in an efficient manner with much fewer learned parameters than that of GAT. We thoroughly evaluate the performance of SpGAT in the semi-supervised node classification task and verified the effectiveness of the learned attentions in the spectral domain.

Includes members:
  Member "power up robust graph convolutional network against evasion attacks based on graph powering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "stabilizing gans with octave convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "a restricted black box adversarial framework towards attacking graph embedding models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey of adversarial learning on graphs")
  Member "lanczosnet multi scale deep graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pushnet efficient and adaptive neural message passing")
  Member "measuring and relieving the over smoothing problem for graph neural networks from the topological view":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "permutohedral gcn graph convolutional networks with global attention")
  Member "graph neural networks with convolutional arma filters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "semi supervised graph classification a hierarchical graph perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robustness verification for transformers")

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)