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: "median activation functions for graph neural networks"
  Abstract: Graph neural networks (GNNs) have been shown to replicate convolutional neural networks' (CNNs) superior performance in many problems involving graphs. By replacing regular convolutions with linear shift-invariant graph filters (LSI-GFs), GNNs take into account the (irregular) structure of the graph and provide meaningful representations of network data. However, LSI-GFs fail to encode local nonlinear graph signal behavior, and so do regular activation functions, which are nonlinear but pointwise. To address this issue, we propose median activation functions with support on graph neighborhoods instead of individual nodes. A GNN architecture with a trainable multirresolution version of this activation function is then tested on synthetic and real-word datasets, where we show that median activation functions can improve GNN capacity with marginal increase in complexity.

Connected through hyperedge HE-1 (induced by source paper "graphon pooling in graph neural networks"):
  with members:
    - "centrality measures for graphons accounting for uncertainty in networks"
    - "geometric deep learning going beyond euclidean data"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "adam a method for stochastic optimization"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-2 (induced by source paper "learning decentralized controllers for robot swarms with graph neural networks"):
  with members:
    - "decentralization of multiagent policies by learning what to communicate"
    - "deep reinforcement learning for swarm systems"
    - "relational forward models for multi agent learning"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "sampling of graph signals with successive local aggregations"
    - "uav assisted heterogeneous networks for capacity enhancement"

Connected through hyperedge HE-3 (induced by source paper "generalizing graph convolutional neural networks with edge variant recursions on graphs"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "topology adaptive graph convolutional networks"
    - "autoregressive moving average graph filtering"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "convolutional neural networks via node varying graph filters"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-4 (induced by source paper "gated graph convolutional recurrent neural networks"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "a recurrent graph neural network for multi relational data"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "stationary signal processing on graphs"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "learning to diagnose with lstm recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a recurrent graph neural network for multi relational data"):
  with members:
    - "convolutional neural networks via node varying graph filters"
    - "geometric deep learning going beyond euclidean data"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "revisiting semi supervised learning with graph embeddings"
    - "sampling of graph signals with successive local aggregations"
    - "tensorflow a system for large scale machine learning"

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)