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: "adapt zero shot adaptive policy transfer for stochastic dynamical systems"
  Abstract: Model-free policy learning has enabled good performance on complex tasks that were previously intractable with traditional control techniques. However, this comes at the cost of requiring a perfectly accurate model for training. This is infeasible due to the very high sample complexity of model-free methods preventing training on the target system. This renders such methods unsuitable for physical systems. Model mismatch due to dynamics parameter differences and unmodeled dynamics error may cause suboptimal or unsafe behavior upon direct transfer. We introduce the Adaptive Policy Transfer for Stochastic Dynamics (AdaPT) algorithm that achieves provably safe and robust, dynamically-feasible zero-shot transfer of RL-policies to new domains with dynamics error. AdaPT combines the strengths of offline policy learning in a black-box source simulator with online tube-based MPC to attenuate bounded dynamics mismatch between the source and target dynamics. AdaPT allows online transfer of policies, trained solely in a simulation offline, to a family of unknown targets without fine-tuning. We also formally show that (i) AdaPT guarantees bounded state and control deviation through state-action tubes under relatively weak technical assumptions and, (ii) AdaPT results in a bounded loss of reward accumulation relative to a policy trained and evaluated in the source environment. We evaluate AdaPT on 2 continuous, non-holonomic simulated dynamical systems with 4 different disturbance models, and find that AdaPT performs between 50 and \(300\%\) better on mean reward accrual than direct policy transfer.

Connected through hyperedge HE-1 (induced by source paper "variance reduction for reinforcement learning in input driven environments"):
  with members:
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "learning to adapt meta learning for model based control"
    - "model based reinforcement learning via meta policy optimization"
    - "flow a modular learning framework for autonomy in traffic"
    - "expert level control of ramp metering based on multi task deep reinforcement learning"
    - "device placement optimization with reinforcement learning"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "barc backward reachability curriculum for robotic reinforcement learning"):
  with members:
    - "learning robotic assembly from cad"
    - "sim to real learning agile locomotion for quadruped robots"
    - "meta learning priors for efficient online bayesian regression"
    - "group marching tree sampling based approximately optimal motion planning on gpus"
    - "automated curriculum learning for neural networks"
    - "reverse curriculum generation for reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "combining model based and model free updates for trajectory centric reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "zero shot deep reinforcement learning driving policy transfer for autonomous vehicles based on robust control"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "progressive neural networks"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "cascade attribute learning network"
    - "proximal policy optimization algorithms"
    - "end to end learning for self driving cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "epopt learning robust neural network policies using model ensembles"

Connected through hyperedge HE-4 (induced by source paper "visual semantic navigation using scene priors"):
  with members:
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "zero shot visual imitation"
    - "interactive grounded language acquisition and generalization in a 2d world"
    - "deepnav learning to navigate large cities"
    - "zero shot task generalization with multi task deep reinforcement learning"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "vision and language navigation interpreting visually grounded navigation instructions in real environments"
    - "visual translation embedding network for visual relation detection"

Connected through hyperedge HE-5 (induced by source paper "data efficient multirobot multitask transfer learning for trajectory tracking"):
  with members:
    - "design of deep neural networks as add on blocks for improving impromptu trajectory tracking"
    - "multi robot transfer learning a dynamical system perspective"
    - "one shot imitation learning"
    - "learning modular neural network policies for multi task and multi robot transfer"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"

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)