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: "probabilistically safe policy transfer"
  Abstract: Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we desire to maximize the expected return while keeping the expected damage below a given safety limit. We study this optimization for the case of a robot manipulator with safety-based torque limits. We would like to ensure that the damage constraint is maintained at every step of the optimization and not just at convergence. To achieve this aim, we introduce a novel method which predicts how modifying the torque limit, as well as how updating the policy parameters, might affect the robot's safety. We show through a number of experiments that our approach allows the robot to improve its performance while ensuring that the expected damage constraint is not violated during the learning process.

Connected through hyperedge HE-1 (induced by source paper "attention privileged reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "state representation learning for control an overview"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "on the continuity of rotation representations in neural networks"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-2 (induced by source paper "risk averse robust adversarial reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "virtual to real reinforcement learning for autonomous driving"
    - "gradient free policy architecture search and adaptation"
    - "parameter space noise for exploration"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "attention privileged reinforcement learning for domain transfer"):
  with members:
    - "attention privileged reinforcement learning"
    - "solving rubik s cube with a robot hand"
    - "simultaneously learning vision and feature based control policies for real world ball in a cup"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"

Connected through hyperedge HE-4 (induced by source paper "predicting stochastic human forward reachable sets based on learned human behavior"):
  with members:
    - "modeling supervisor safe sets for improving collaboration in human robot teams"

Connected through hyperedge HE-5 (induced by source paper "following high level navigation instructions on a simulated quadcopter with imitation learning"):
  with members:
    - "unifying map and landmark based representations for visual navigation"
    - "teaching uavs to race end to end regression of agile controls in simulation"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "memory augmented control networks"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "grounded language learning in a simulated 3d world"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "densely connected convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "accelerated primal dual policy optimization for safe reinforcement learning"):
  with members:
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "constrained policy optimization"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "safe policy search for lifelong reinforcement learning with sublinear regret"

Connected through hyperedge HE-7 (induced by source paper "asymmetric actor critic for image based robot learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "deep predictive policy training using reinforcement learning"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-8 (induced by source paper "constrained policy optimization"):
  with members:
    - "deep reinforcement learning for autonomous driving"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "asynchronous methods for deep reinforcement learning"
    - "safe multi agent reinforcement learning for autonomous driving"
    - "q prop sample efficient policy gradient with an off policy critic"
    - "benchmarking deep reinforcement learning for continuous control"
    - "doubly robust off policy value evaluation for reinforcement learning"
    - "high dimensional continuous control using generalized advantage estimation"

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)