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: "graph policy gradients for large scale robot control"
  Abstract: In this paper, we consider the problem of learning policies to control a large number of homogeneous robots. To this end, we propose a new algorithm we call Graph Policy Gradients (GPG) that exploits the underlying graph symmetry among the robots. The curse of dimensionality one encounters when working with a large number of robots is mitigated by employing a graph convolutional neural (GCN) network to parametrize policies for the robots. The GCN reduces the dimensionality of the problem by learning filters that aggregate information among robots locally, similar to how a convolutional neural network is able to learn local features in an image. Through experiments on formation flying, we show that our proposed method is able to scale better than existing reinforcement methods that employ fully connected networks. More importantly, we show that by using our locally learned filters we are able to zero-shot transfer policies trained on just three robots to over hundred robots.

Connected through hyperedge HE-1 (induced by source paper "inner attention supported adaptive cooperation for heterogeneous multi robots teaming based on multi agent reinforcement learning"):
  with members:
    - "learning agile and dynamic motor skills for legged robots"
    - "enabling intuitive human robot teaming using augmented reality and gesture control"
    - "cleaning tasks knowledge transfer between heterogeneous robots a deep learning approach"
    - "zero shot autonomous vehicle policy transfer from simulation to real world via adversarial learning"
    - "a comprehensive review of shepherding as a bio inspired swarm robotics guidance approach"
    - "learning attentional communication for multi agent cooperation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"

Connected through hyperedge HE-2 (induced by source paper "scaling up multiagent reinforcement learning for robotic systems learn an adaptive sparse communication graph"):
  with members:
    - "adaptively sparse transformers"
    - "learning transferable cooperative behavior in multi agent teams"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "actor attention critic for multi agent reinforcement learning"
    - "mean field multi agent reinforcement learning"
    - "graph convolutional reinforcement learning for multi agent cooperation"
    - "on controllable sparse alternatives to softmax"

Connected through hyperedge HE-3 (induced by source paper "represented value function approach for large scale multi agent reinforcement learning"):
  with members:
    - "stability properties of graph neural networks"
    - "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-4 (induced by source paper "where are the keys learning object centric navigation policies on semantic maps with graph convolutional networks"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "representation learning on graphs a reinforcement learning application"
    - "learning heuristics over large graphs via deep reinforcement learning"
    - "deep slam object level rgbd slam based on class specific deep shape priors"
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "fusion volumetric object level slam"

Connected through hyperedge HE-5 (induced by source paper "graph policy gradients for large scale unlabeled motion planning with constraints"):
  with members:
    - "stability of graph scattering transforms"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "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)