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: "learning decentralized controllers for robot swarms with graph neural networks"
  Abstract: We consider the problem of finding distributed controllers for large networks of mobile robots with interacting dynamics and sparsely available communications. Our approach is to learn local controllers which require only local information and local communications at test time by imitating the policy of centralized controllers using global information at training time. By extending aggregation graph neural networks to time varying signals and time varying network support, we learn a single common local controller which exploits information from distant teammates using only local communication interchanges. We apply this approach to a decentralized linear quadratic regulator problem and observe how faster communication rates and smaller network degree increase the value of multi-hop information. Separate experiments learning a decentralized flocking controller demonstrate performance on communication graphs that change as the robots move.

Connected through hyperedge HE-1 (induced by source paper "graphs convolutions and neural networks"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "stability properties of 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-2 (induced by source paper "graph neural networks for decentralized multi robot path planning"):
  with members:
    - "multi robot path deconfliction through prioritization by path prospects"
    - "stability properties of 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-3 (induced by source paper "stability properties of graph neural networks"):
  with members:
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "diffusion scattering transforms on graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-4 (induced by source paper "stability of graph neural networks to relative perturbations"):
  with members:
    - "controllability of bandlimited graph processes over random time varying graphs"
    - "gated graph convolutional recurrent neural networks"
    - "stability properties of graph 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"

Connected through hyperedge HE-5 (induced by source paper "stability of graph scattering transforms"):
  with members:
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks via scattering"
    - "diffusion scattering transforms on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "network topology inference from spectral templates"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "authorship attribution through function word adjacency networks"
    - "discrete signal processing on graphs frequency analysis"

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.RO(Robotics)