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: "stability properties of graph neural networks"
  Abstract: Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear processing of graph signals, exhibiting success in traffic forecasting, recommender systems, power outage prediction, and motion planning, among others. GNNs consist of a cascade of layers, each of which applies a graph convolution, followed by a pointwise nonlinearity. In this work, we study the impact that changes in the underlying topology have on the output of the GNN. First, we show that GNNs are permutation equivariant, which implies that they effectively exploit internal symmetries of the underlying topology. Then, we prove that graph convolutions with integral Lipschitz filters, in combination with the frequency mixing effect of the corresponding nonlinearities, yields an architecture that is both stable to small changes in the underlying topology and discriminative of information located at high frequencies. These are two properties that cannot simultaneously hold when using only linear graph filters, which are either discriminative or stable, thus explaining the superior performance of GNNs.

Connected through hyperedge HE-1 (induced by source paper "edgenets edge varying graph neural networks"):
  with members:
    - "dfnets spectral cnns for graphs with feedback looped filters"
    - "graph neural networks with convolutional arma filters"
    - "on the transferability of spectral graph filters"
    - "generalizing graph convolutional neural networks with edge variant recursions on graphs"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "represented value function approach for large scale multi agent reinforcement learning"):
  with members:
    - "graph policy gradients for large scale robot control"
    - "concurrent meta reinforcement learning"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "counterfactual multi agent policy gradients"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-3 (induced by source paper "graphs convolutions and neural networks"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "autoregressive moving average graph filtering"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "transferability of spectral graph convolutional neural networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "stability of graph scattering transforms"
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "diffusion scattering transforms on graphs"
    - "autoregressive moving average graph filtering"
    - "filtering random graph processes over random time varying graphs"

Connected through hyperedge HE-5 (induced by source paper "graph policy gradients for large scale robot control"):
  with members:
    - "concurrent meta reinforcement learning"
    - "a comprehensive survey on graph neural networks"
    - "scalable centralized deep multi agent reinforcement learning via policy gradients"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "counterfactual multi agent policy gradients"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "memory augmented control networks"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "graph neural networks for decentralized multi robot path planning"):
  with members:
    - "multi robot path deconfliction through prioritization by path prospects"
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "primal pathfinding via reinforcement and imitation multi agent learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "graph neural networks for learning robot team coordination"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "neural message passing for quantum chemistry"
    - "towards generalization and simplicity in continuous control"

Connected through hyperedge HE-7 (induced by source paper "stability of graph neural networks to relative perturbations"):
  with members:
    - "controllability of bandlimited graph processes over random time varying graphs"
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "gated graph convolutional recurrent neural networks"
    - "graph convolutional neural networks via scattering"
    - "diffusion scattering transforms on graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "network topology inference from spectral templates"
    - "semi supervised classification with graph convolutional networks"

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)