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 safe unlabeled multi robot planning with motion constraints"
  Abstract: In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations.

Connected through hyperedge HE-1 (induced by source paper "glas global to local safe autonomy synthesis for multi robot motion planning with end to end learning"):
  with members:
    - "trajectory generation for multiagent point to point transitions via distributed model predictive control"
    - "control regularization for reduced variance reinforcement learning"
    - "neural swarm decentralized close proximity multirotor controlusing learned interactions"
    - "graph neural networks for decentralized multi robot path planning"
    - "graph policy gradients for large scale unlabeled motion planning with constraints"
    - "pytorch an imperative style high performance deep learning library"
    - "differentiable particle filters end to end learning with algorithmic priors"

Connected through hyperedge HE-2 (induced by source paper "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving"):
  with members:
    - "a fleet of miniature cars for experiments in cooperative driving"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "graph policy gradients for large scale unlabeled motion planning with constraints"):
  with members:
    - "stability of graph scattering transforms"
    - "graph policy gradients for large scale robot control"
    - "a comprehensive survey on graph neural networks"
    - "learning sample efficient target reaching for mobile robots"
    - "semi supervised classification with graph convolutional networks"
    - "distance optimal formation control on graphs with a tight convergence time guarantee"

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)