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: "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
  Abstract: In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, our policy directly maps raw sensor measurements to an agent's steering commands in terms of the movement velocity. As a first step toward reducing the performance gap between decentralized and centralized methods, we present a multi-scenario multi-stage training framework to learn an optimal policy. The policy is trained over a large number of robots in rich, complex environments simultaneously using a policy gradient based reinforcement learning algorithm. The learning algorithm is also integrated into a hybrid control framework to further improve the policy's robustness and effectiveness. #R##N#We validate the learned sensor-level collision avoidance policy in a variety of simulated and real-world scenarios with thorough performance evaluations for large-scale multi-robot systems. The generalization of the learned policy is verified in a set of unseen scenarios including the navigation of a group of heterogeneous robots and a large-scale scenario with 100 robots. Although the policy is trained using simulation data only, we have successfully deployed it on physical robots with shapes and dynamics characteristics that are different from the simulated agents, in order to demonstrate the controller's robustness against the sim-to-real modeling error. Finally, we show that the collision-avoidance policy learned from multi-robot navigation tasks provides an excellent solution to the safe and effective autonomous navigation for a single robot working in a dense real human crowd. Our learned policy enables a robot to make effective progress in a crowd without getting stuck. Videos are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "frozone freezing free pedestrian friendly navigation in human crowds"):
  with members:
    - "densecavoid real time navigation in dense crowds using anticipatory behaviors"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "learning resilient behaviors for navigation under uncertainty environments"):
  with members:
    - "a general framework for uncertainty estimation in deep learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "learning navigation behaviors end to end with autorl"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "learning to drive in a day"

Connected through hyperedge HE-4 (induced by source paper "ctrl z recovering from instability in reinforcement learning"):
  with members:
    - "learning gentle object manipulation with curiosity driven deep reinforcement learning"
    - "optimal off policy evaluation for reinforcement learning with marginalized importance sampling"
    - "off policy evaluation via off policy classification"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "progressive neural networks"

Connected through hyperedge HE-5 (induced by source paper "deepmnavigate deep reinforced multi robot navigation unifying local global collision avoidance"):
  with members:
    - "densecavoid real time navigation in dense crowds using anticipatory behaviors"
    - "learning to communicate a machine learning framework for heterogeneous multi agent robotic systems"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"

Connected through hyperedge HE-6 (induced by source paper "reciprocal collision avoidance for general nonlinear agents using reinforcement learning"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "alan adaptive learning for multi agent navigation"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "safe reinforcement learning with model uncertainty estimates"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "spatial uncertainty sampling for end to end control"
    - "stein variational policy gradient"
    - "gp ilqg data driven robust optimal control for uncertain nonlinear dynamical systems"
    - "social attention modeling attention in human crowds"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "socially aware motion planning with deep reinforcement learning"
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"

Connected through hyperedge HE-8 (induced by source paper "getting robots unfrozen and unlost in dense pedestrian crowds"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "proximal policy optimization algorithms"
    - "markov localization for mobile robots in dynamic environments"

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)